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@article{Madani2016,
abstract = {Traffic Sign Detection and Recognition (TSDR) plays a crucial role in driver assistance systems, and provides drivers with safety and precaution information. It is part of the computer vision which requires a dataset for training and testing the detection and recognition techniques. In this paper, a dataset for Malaysian TS (MTSD) is proposed in order to eliminate the gap in the previously created datasets. The MTSD includes a variety of TS scenes to be used in TS detection and images contain only TS to assist in the recognition of TS.},
author = {Madani, Ahmed and Yusof, Rubiyah},
file = {:C$\backslash$:/Users/cairo/Documents/F5F5/Thesis/utmthesis-master/odd{\_}citation/1423-3991-1-SM.pdf:pdf},
issn = {22898131},
journal = {Journal of Telecommunication, Electronic},
number = {11},
pages = {137--143},
title = {{Malaysian Traffic Sign Dataset for Traffic Sign Detection and Recognition Systems}},
url = {file:///C:/Users/Hossam Elhawary/Downloads/1423-3991-1-SM.pdf},
volume = {8},
year = {2016}
}
@inproceedings{Long2018,
abstract = {Developing a safe and efficient collision avoidance policy for multiple robots is challenging in the decentralized scenarios where each robot generates its paths without observing other robots' states and intents. While other distributed multi-robot collision avoidance systems exist, they often require extracting agent-level features to plan a local collision-free action, which can be computationally prohibitive and not robust. More importantly, in practice the performance of these methods are much lower than their centralized counterparts. We present a decentralized sensor-level collision avoidance policy for multi-robot systems, which directly maps raw sensor measurements to an agent's steering commands in terms of movement velocity. As a first step toward reducing the performance gap between decentralized and centralized methods, we present a multi-scenario multi-stage training framework to learn an optimal policy. The policy is trained over a large number of robots on rich, complex environments simultaneously using a policy gradient based reinforcement learning algorithm. We validate the learned sensor-level collision avoidance policy in a variety of simulated scenarios with thorough performance evaluations and show that the final learned policy is able to find time efficient, collision-free paths for a large-scale robot system. We also demonstrate that the learned policy can be well generalized to new scenarios that do not appear in the entire training period, including navigating a heterogeneous group of robots and a large-scale scenario with 100 robots. Videos are available at https://sites.google.com/view/drlmaca.},
archivePrefix = {arXiv},
arxivId = {1709.10082v3},
author = {Long, Pinxin and Fan, Tingxiang and Liao, Xinyi and Liu, Wenxi and Zhang, Hao and Pan, Jia},
booktitle = {International Conference on Robotics and Automation (ICRA)},
eprint = {1709.10082v3},
file = {:C$\backslash$:/Users/cairo/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Long et al. - Unknown - Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning(2).pdf:pdf},
title = {{Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning}},
url = {https://sites.google.com/view/drlmaca.},
year = {2018}
}
@inproceedings{tongoly2017,
author = {Tongloy, T. and Chuwongin, S. and Jaksukam, K. and Chousangsuntorn, C and Boonsang, S.},
booktitle = {2017 2nd International Conference on Robotics and Automation Engineering (ICRAE)},
doi = {10.1109/ICRAE.2017.8291355},
file = {:C$\backslash$:/Users/cairo/Downloads/iee drl mobile robot.pdf:pdf},
isbn = {978-1-5386-1306-1},
keywords = {drl,masters},
mendeley-tags = {drl,masters},
month = {dec},
pages = {68--72},
publisher = {IEEE},
title = {{Asynchronous deep reinforcement learning for the mobile robot navigation with supervised auxiliary tasks}},
url = {http://ieeexplore.ieee.org/document/8291355/},
year = {2017}
}
@phdthesis{Wooden2006,
author = {Wooden, David T},
file = {:C$\backslash$:/Users/cairo/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Wooden - 2006 - Graph-based Path Planning for Mobile Robots.pdf:pdf},
keywords = {masters,path planning},
mendeley-tags = {masters,path planning},
number = {December},
school = {School of Electrical and Computer Engineering, Georgia Institute of Technology},
title = {{Graph-based Path Planning for Mobile Robots Graph-based Path Planning for Mobile Robots}},
url = {https://smartech.gatech.edu/bitstream/handle/1853/14055/wooden{\_}david{\_}t{\_}200611{\_}phd.pdf?sequence=1{\&}isAllowed=y},
year = {2006}
}
@article{Fei-Fei2006,
abstract = {Learning visual models of object categories notoriously requires hundreds or thousands of training examples. We show that it is possible to learn much information about a category from just one, or a handful, of images. The key insight is that, rather than learning from scratch, one can take advantage of knowledge coming from previously learned categories, no matter how different these categories might be. We explore a Bayesian implementation of this idea. Object categories are represented by probabilistic models. Prior knowledge is represented as a probability density function on the parameters of these models. The posterior model for an object category is obtained by updating the prior in the light of one or more observations. We test a simple implementation of our algorithm on a database of 101 diverse object categories. We compare category models learned by an implementation of our Bayesian approach to models learned from by Maximum Likelihood (ML) and Maximum A Posteriori (MAP) methods. We find that on a database of more than 100 categories, the Bayesian approach produces informative models when the number of training examples is too small for other methods to operate successfully.},
author = {Fei-Fei, Li and Fergus, Rob and Perona, Pietro},
doi = {10.1109/TPAMI.2006.79},
file = {:C$\backslash$:/Users/cairo/Downloads/fifi2.pdf:pdf},
isbn = {0162-8828 VO - 28},
issn = {01628828},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {Few images,Learning,Object categories,Priors,Recognition,Unsupervised,Variational inference},
number = {4},
pages = {594--611},
pmid = {16566508},
title = {{One-shot learning of object categories}},
volume = {28},
year = {2006}
}
@article{Hsu2006,
abstract = {Why are probabilistic roadmap (PRM) planners "probabilistic"? This paper tries to establish the probabilistic foundations of PRM planning and reexamines previous work in this context. It shows that the success of PRM planning depends mainly and critically on the assumption that the configuration space C of a robot often verifies favorable "visibility" properties that are not directly dependent on the dimensionality of C. A promising way of speeding up PRM planners is to extract partial knowledge on such properties during roadmap construction and use this knowledge to adjust the sampling measure continuously. This paper also shows that the choice of the sampling source-pseudo-random or deterministic-has small impact on a PRM planner's performance, compared to that of the sampling measure. These conclusions are supported by both theoretical arguments and empirical results.},
author = {Hsu, David and Latombe, Jean-Claude and Kurniawati, Hanna},
doi = {https://doi.org/10.1177/0278364906067174},
file = {:C$\backslash$:/Users/cairo/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Hsu, Latombe, Kurniawati - 2006 - On the Probabilistic Foundations of Probabilistic Roadmap Planning.pdf:pdf},
journal = {The International Journal of Robotics Research},
keywords = {masters,prm},
mendeley-tags = {masters,prm},
number = {7},
pages = {627--643},
title = {{On the Probabilistic Foundations of Probabilistic Roadmap Planning}},
url = {http://www.mit.edu/{~}hannakur/papers/isrr05{\_}prmFoundation.pdf},
volume = {25},
year = {2006}
}
@article{Young8416973,
abstract = {Deep learning methods employ multiple processing layers to learn hierarchical representations of data and have produced state-of-the-art results in many domains. Recently, a variety of model designs and methods have blossomed in the context of natural language processing (NLP). In this paper, we review significant deep learning related models and methods that have been employed for numerous NLP tasks and provide a walk-through of their evolution. We also summarize, compare and contrast the various models and put forward a detailed understanding of the past, present and future of deep learning in NLP.},
archivePrefix = {arXiv},
arxivId = {1708.02709},
author = {Young, Tom and Hazarika, Devamanyu and Poria, Soujanya and Cambria, Erik},
doi = {10.1109/MCI.2018.2840738},
eprint = {1708.02709},
file = {:C$\backslash$:/Users/cairo/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Young et al. - Unknown - Recent Trends in Deep Learning Based Natural Language Processing.pdf:pdf},
issn = {1556-603X},
journal = {IEEE Computational Intelligence Magazine},
keywords = {Computational modeling,Context modeling,Learning systems,Machine learning,NLP tasks,Natural language processing,Semantics,data structures,deep learning methods,deep learning related models,hierarchical data representations,learning (artificial intelligence),masters,model designs,multiple processing layers,natural language processing,nlp},
mendeley-tags = {masters,nlp},
month = {aug},
number = {3},
pages = {55--75},
title = {{Recent Trends in Deep Learning Based Natural Language Processing}},
url = {http://veredshwartz.blogspot.sg. http://arxiv.org/abs/1708.02709},
volume = {13},
year = {2018}
}
@article{Farouk2014,
abstract = {There are many slaughter procedures that religions and cultures use around the world. The two that are commercially relevant are the halal and kosher methods practiced by Muslims and Jews respectively. The global trade in red meat and poultry produced using these two methods is substantial, thus the importance of the quality of the meat produced using the methods. Halal and kosher slaughter per se should not affect meat quality more than their industrial equivalents, however, some of their associated pre- and post-slaughter processes do. For instance, the slow decline in blood pressure following a halal pre-slaughter head-only stun and neck cut causes blood splash (ecchymosis) in a range of muscles and organs of slaughtered livestock. Other quality concerns include bruising, hemorrhages, skin discoloration and broken bones particularly in poultry. In addition to these conventional quality issues, the "spiritual quality" of the meat can also be affected when the halal and kosher religious requirements are not fully met during the slaughter process. The nature, causes, importance and mitigations of these and other quality issues related to halal and kosher slaughtering and meat production using these methods are the subjects of this review. {\textcopyright} 2014 Elsevier Ltd.},
author = {Farouk, M. M. and Al-Mazeedi, H. M. and Sabow, A. B. and Bekhit, A. E.D. and Adeyemi, K. D. and Sazili, A. Q. and Ghani, A.},
doi = {10.1016/j.meatsci.2014.05.021},
file = {:C$\backslash$:/Users/cairo/Downloads/Halal and kosher slaughter methods and meat quality A review.pdf:pdf},
isbn = {1873-4138 (Electronic)$\backslash$r0309-1740 (Linking)},
issn = {03091740},
journal = {Meat Science},
keywords = {Meat spiritual quality,Nutrition,Ritual slaughter,Safety,Stunning},
number = {3},
pages = {505--519},
pmid = {24973207},
publisher = {Elsevier Ltd},
title = {{Halal and kosher slaughter methods and meat quality: A review}},
url = {http://dx.doi.org/10.1016/j.meatsci.2014.05.021},
volume = {98},
year = {2014}
}
@article{Sünderhauf2018,
abstract = {The application of deep learning in robotics leads to very specific problems and research questions that are typically not addressed by the computer vision and machine learning communities. In this paper we discuss a number of robotics-specific learning, reasoning, and embodiment challenges for deep learning. We explain the need for better evaluation metrics, highlight the importance and unique challenges for deep robotic learning in simulation, and explore the spectrum between purely data-driven and model-driven approaches. We hope this paper provides a motivating overview of important research directions to overcome the current limitations, and helps to fulfill the promising potentials of deep learning in robotics.},
archivePrefix = {arXiv},
arxivId = {arXiv:1804.06557v1},
author = {S{\"{u}}nderhauf, Niko and Brock, Oliver and Scheirer, Walter and Hadsell, Raia and Fox, Dieter and Leitner, J{\"{u}}rgen and Upcroft, Ben and Abbeel, Pieter and Burgard, Wolfram and Milford, Michael and Corke, Peter},
doi = {10.1177/0278364918770733},
eprint = {arXiv:1804.06557v1},
file = {:C$\backslash$:/Users/cairo/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/S{\"{u}}nderhauf et al. - Unknown - The Limits and Potentials of Deep Learning for Robotics.pdf:pdf},
isbn = {0309324718},
journal = {The International Journal of Robotics Research},
keywords = {drl,masters},
mendeley-tags = {drl,masters},
number = {4-5},
pages = {405--420},
title = {{The Limits and Potentials of Deep Learning for Robotics}},
url = {https://arxiv.org/pdf/1804.06557.pdf http://arxiv.org/abs/1804.06557 https://doi.org/10.1177/0278364918770733},
volume = {37},
year = {2018}
}
@article{Nazri2012,
abstract = {The study was carried out to assess the compliance of the chicken slaughterhouses management toward Halal Certification Standard and Condition. This study used two different methods; face-to-face interviews, and participation and individual observational surveys. Interviews involved personnel from JHEAINS, JAKIM Sabah Branch, Sabah State MGI Committee, and the owner of halal chicken slaughterhouses. Participating observations on slaughterhouses was performed with cooperation of MGI Committee Staff under the Halal Monitoring Schedules, meanwhile individual observations was done personally after obtaining permission from slaughterhouses personnel. The result showed that there were weaknesses in filing record system and halal monitoring by JHEAINS, including differentiation of guidelines and terms of Islamic Animal Slaughter and slaughterhouse scales compared to JAKIM. All slaughterhouses in study were found to comply with halal certification documentation system and halal chicken processing. Meanwhile, Halal inspectors from RSA, RSB, and RSC leave their location without replacement and did not perform detail observation on post slaughtered chicken. Keywo},
author = {Nazri, Mohd and Rahman, Abdul and Md.Shaarani, Sharifudin},
file = {:C$\backslash$:/Users/cairo/Downloads/FullPaperMohdNazriAbdulRahmanHalalSlaughtering-ICFSN2012aspublishedinICFSN2012.pdf:pdf},
journal = {International Conference on Food Science and Nutrition},
keywords = {halal certification standard,islamic animal slaughter,jakim,jheains,mgi},
number = {April},
pages = {895--918},
title = {{Study on the Effectiveness of Halal Certification in Slaughtering House Around Kota Kinabalu , Sabah}},
year = {2012}
}
@article{Gerkey2004,
abstract = {Despite more than a decade of experimental work in multi-robot systems, important theoretical aspects of multi-robot coordination mechanisms have, to date, been largely untreated. To address this issue, we focus on the problem of multi-robot task allocation (MRTA). Most work on MRTA has been ad hoc and empirical, with many coordination architectures having been proposed and validated in a proof-of-concept fashion, but infrequently analyzed. With the goal of bringing objective grounding to this important area of research, we present a formal study of MRTA problems. A domain-independent taxonomy of MRTA problems is given, and it is shown how many such problems can be viewed as instances of other, well-studied, optimization problems. We demonstrate how relevant theory from operations research and combinatorial optimization can be used for analysis and greater understanding of existing approaches to task allocation, and to show how the same theory can be used in the synthesis of new approaches.},
author = {Gerkey, Brian P. and Matari{\'{c}}, Maja J.},
doi = {10.1177/0278364904045564},
file = {:C$\backslash$:/Users/cairo/Downloads/gerkey2004.pdf:pdf},
isbn = {0278-3649},
issn = {02783649},
journal = {International Journal of Robotics Research},
keywords = {Coordination,Multi-robot systems,Task allocation,Utility},
number = {9},
pages = {939--954},
title = {{A formal analysis and taxonomy of task allocation in multi-robot systems}},
volume = {23},
year = {2004}
}
@inproceedings{Peres2017,
author = {Peres, Janilma and Rosa, Paulo Fernando F. and Choren, Ricardo},
booktitle = {2017 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS)},
doi = {10.1109/IRIS.2017.8250110},
file = {:C$\backslash$:/Users/cairo/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Peres, Rosa, Choren - 2017 - A multi-agent architecture for swarm robotics systems.pdf:pdf},
isbn = {978-1-5386-1342-9},
keywords = {masters,swarm},
mendeley-tags = {masters,swarm},
month = {oct},
pages = {130--135},
publisher = {IEEE},
title = {{A multi-agent architecture for swarm robotics systems}},
url = {http://ieeexplore.ieee.org/document/8250110/},
year = {2017}
}
@article{Ferrante2013,
abstract = {In this paper we propose GESwarm, a novel tool that$\backslash$ncan automatically synthesise collective behaviours for$\backslash$nswarms of autonomous robots through evolutionary$\backslash$nrobotics. Evolutionary robotics typically relies on$\backslash$nartificial evolution for tuning the weights of an$\backslash$nartificial neural network that is then used as$\backslash$nindividual behaviour representation. The main caveat of$\backslash$nneural networks is that they are very difficult to$\backslash$nreverse engineer, meaning that once a suitable solution$\backslash$nis found, it is very difficult to analyse, to modify,$\backslash$nand to tease apart the inherent principles that lead to$\backslash$nthe desired collective behaviour. In contrast, our$\backslash$nrepresentation is based on completely readable and$\backslash$nanalysable individual-level rules that lead to a$\backslash$ndesired collective behaviour.$\backslash$n$\backslash$nThe core of our method is a grammar that can generate a$\backslash$nrich variety of collective behaviours. We test GESwarm$\backslash$nby evolving a foraging strategy using a realistic swarm$\backslash$nrobotics simulator. We then systematically compare the$\backslash$nevolved collective behaviour against an hand-coded one$\backslash$nfor performance, scalability and flexibility, showing$\backslash$nthat collective behaviours evolved with GESwarm can$\backslash$noutperform the hand-coded one.},
author = {Ferrante, Eliseo and Duenez-Guzman, Edgar and Turgut, Ali Emre and Wenseleers, Tom},
doi = {doi:10.1145/2463372.2463385},
file = {:C$\backslash$:/Users/cairo/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ferrante et al. - 2013 - GESwarm grammatical evolution for the automatic synthesis of collective behaviors in swarm robotics.pdf:pdf},
isbn = {9781450319638},
journal = {GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference},
keywords = {genetic algorithms,genetic programming,grammatical evolution,masters,swarm},
mendeley-tags = {masters,swarm},
pages = {17--24},
pmid = {1005163},
title = {{GESwarm: grammatical evolution for the automatic synthesis of collective behaviors in swarm robotics}},
year = {2013}
}
@article{Rimon1992,
abstract = {A methodology for exact robot motion planning and control that unifies the purely kinematic path planning problem with the lower level feedback controller design is presented. Complete information about a freespace and goal is encoded in the form of a special artificial potential function, called a navigation function, that connects the kinematic planning problem with the dynamic execution problem in a provably correct fashion. The navigation function automatically gives rise to a bounded-torque feedback controller for the robot's actuators that guarantees collision-free motion and convergence to the destination from almost all initial free configurations. A formula for navigation functions that guide a point-mass robot in a generalized sphere world is developed. The simplest member of this family is a space obtained by puncturing a disk by an arbitrary number of smaller disjoint disks representing obstacles. The other spaces are obtained from this model by a suitable coordinate transformation. Simulation results for planar scenarios are provided},
author = {Rimon, Elon and Koditschek, Daniel},
doi = {10.1109/70.163777},
file = {:C$\backslash$:/Users/cairo/Downloads/potential fields.pdf:pdf},
issn = {1042296X},
journal = {Robotics and Automation, IEEE},
keywords = {Masters,artificial potential fields,path planning,potential fields},
mendeley-tags = {Masters,artificial potential fields,path planning,potential fields},
number = {5},
pages = {501--518},
title = {{Exact Robot Navigation Using Artificial Potential Functions}},
url = {http://ieeexplore.ieee.org/xpls/abs{\_}all.jsp?arnumber=163777},
volume = {8},
year = {1992}
}
@article{AbuEl-Sebah2010,
author = {{Abu El-Sebah}, Mohamed I.},
doi = {10.6113/JPE.2010.10.2.171},
issn = {1598-2092},
journal = {Journal of Power Electronics},
month = {mar},
number = {2},
pages = {171--175},
title = {{PMSM Position Control with a SUI PID Controller}},
url = {https://www.researchgate.net/publication/264187988{\_}Pmsm{\_}position{\_}control{\_}with{\_}a{\_}SUI{\_}PID{\_}controller},
volume = {10},
year = {2010}
}
@article{Zhu2006,
abstract = {The artificial potential field (APF) approach provides a simple and effective motion planning method for practical purpose. However, artificial potential field approach has a major problem, which is that the robot is easy to be trapped at a local minimum before reaching its goal. The avoidance of local minimum has been an active research topic in path planning by potential field. In this paper, we introduce several methods to solve this problem, emphatically, introduce and evaluate the artificial potential field approach with simulated annealing (SA). As one of the powerful techniques for escaping local minimum, simulated annealing has been applied to local and global path planning},
author = {Zhu, Qidan and Yan, Yongjie and Xing, Zhuoyi},
doi = {10.1109/ISDA.2006.253908},
file = {:C$\backslash$:/Users/cairo/Downloads/04021735.pdf:pdf},
isbn = {0-7695-2528-8},
issn = {02635747},
journal = {Sixth International Conference on Intelligent Systems Design and Applications},
keywords = {Circuit simulation,Educational institutions,Heuristic algorithms,Motion planning,Orbital robotics,Partitioning algorithms,Path planning,Robotics and automation,Robots,Simulated annealing,artificial potential field,mobile robots,motion planning,path planning,robot path planning,simulated annealing},
pages = {622--627},
title = {{Robot Path Planning Based on Artificial Potential Field Approach with Simulated Annealing}},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4021735},
volume = {2},
year = {2006}
}
@article{Hart1968,
abstract = {Although the problem of determining the minimum cost path through a graph arises naturally in a number of interesting applications, there has been no underlying theory to guide the development of efficient search procedures. Moreover, there is no adequate conceptual framework within which the various ad hoc search strategies proposed to date can be compared. This paper describes how heuristic information from the problem domain can be incorporated into a formal mathematical theory of graph searching and demonstrates an optimality property of a class of search strategies.},
author = {Hart, P E and Nilsson, N J and Raphael, B},
doi = {10.1109/TSSC.1968.300136},
issn = {0536-1567},
journal = {IEEE Transactions on Systems Science and Cybernetics},
keywords = {A* RouteOptimisation autonomy navigation search},
month = {jul},
number = {2},
pages = {100--107},
title = {{A Formal Basis for the Heuristic Determination of Minimum Cost Paths}},
volume = {4},
year = {1968}
}
@article{Silver2014,
abstract = {In this paper we consider deterministic policy gradient algorithms for reinforcement learning with continuous actions. The deterministic pol- icy gradient has a particularly appealing form: it is the expected gradient of the action-value func- tion. This simple form means that the deter- ministic policy gradient can be estimated much more efficiently than the usual stochastic pol- icy gradient. To ensure adequate exploration, we introduce an off-policy actor-critic algorithm that learns a deterministic target policy from an exploratory behaviour policy. We demonstrate that deterministic policy gradient algorithms can significantly outperform their stochastic counter- parts in high-dimensional action spaces.},
author = {Silver, David and Lever, Guy and Heess, Nicolas and Degris, Thomas and Wierstra, Daan and Riedmiller, Martin},
file = {:C$\backslash$:/Users/cairo/Documents/F5F5/proposal/silver14.pdf:pdf},
isbn = {9781634393973},
issn = {1938-7228},
journal = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
pages = {387--395},
title = {{Deterministic Policy Gradient Algorithms}},
year = {2014}
}
@article{Brambilla2013,
abstract = {Swarm robotics is an approach to collective robotics that takes inspiration from the self-organized behaviors of social animals. Through simple rules and local interactions, swarm robotics aims at designing robust, scalable, and flexible collective behaviors for the coordination of large numbers of robots. In this paper, we analyze the literature from the point of view of swarm engineering: we focus mainly on ideas and concepts that contribute to the advancement of swarm robotics as an engineering field and that could be relevant to tackle real-world applications. Swarm engineering is an emerging discipline that aims at defining systematic and well founded procedures for modeling, designing, realizing, verifying, validating, operating, and maintaining a swarm robotics system. We propose two taxonomies: in the first taxonomy, we classify works that deal with design and analysis methods; in the second taxonomy, we classify works according to the collective behavior studied. We conclude with a discussion of the current limits of swarm robotics as an engineering discipline and with suggestions for future research directions.},
author = {Brambilla, Manuele and Ferrante, Eliseo and Birattari, Mauro and Dorigo, Marco},
doi = {10.1007/s11721-012-0075-2},
file = {:C$\backslash$:/Users/cairo/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Brambilla et al. - 2013 - Swarm robotics A review from the swarm engineering perspective.pdf:pdf},
isbn = {1935-3812},
issn = {19353812},
journal = {Swarm Intelligence},
keywords = {Review,Swarm engineering,Swarm robotics,masters,review paper,swarm},
mendeley-tags = {masters,review paper,swarm},
month = {mar},
number = {1},
pages = {1--41},
publisher = {Springer US},
title = {{Swarm robotics: A review from the swarm engineering perspective}},
url = {http://link.springer.com/10.1007/s11721-012-0075-2},
volume = {7},
year = {2013}
}
@article{Zhang2010,
abstract = {Collision between robots and the global minimum point, which exist in the traditional artificial potential field (TAPF), seriously affect the formation of multi-robot system. The paper constructs a dynamic artificial potential field (DAPF) based on the local information to solve the problem of the TAPF. Collisions between robots are avoided by introducing the repulsion between robots. The problem of global minimum point is solved by adjusting the robot movement direction within the escaping area. Simulation results demonstrate the effectiveness and practicability of the formation control algorithm based on the proposed dynamic artificial potential field.},
author = {Zhang, Min and Shen, Yi and Wang, Qiang and Wang, Yibo},
doi = {10.1109/IMTC.2010.5488238},
file = {:C$\backslash$:/Users/cairo/Downloads/potential field.pdf:pdf},
isbn = {978-1-4244-2832-8},
issn = {1091-5281},
journal = {2010 IEEE Instrumentation {\&} Measurement Technology Conference Proceedings},
keywords = {artificial potential field,global minimum point,multi-robot formation},
number = {1},
pages = {1530--1534},
title = {{Dynamic artificial potential field based multi-robot formation control}},
url = {http://ieeexplore.ieee.org/document/5488238/},
volume = {2},
year = {2010}
}
@article{Yang2004,
abstract = {Multiagent reinforcement learning for multi- robot systems is a challenging issue in both robotics and artificial intelligence. With the ever increasing interests in theoretical researches and practical applications, currently there have been a lot of efforts towards providing some solutions to this challenge. However, there are still many difficulties in scaling up the multiagent reinforcement learning to multi-robot systems. The main objective of this paper is to provide a survey, though not completely on the multiagent reinforcement learning in multi-robot systems. After reviewing important advances in this field, some challenging problems and promising research directions are analyzed. A concluding remark is made from the perspectives of the authors. I.},
author = {Yang, Erfu and Gu, Dongbing},
doi = {10.1.1.2.1602},
file = {:C$\backslash$:/Users/cairo/Documents/F5F5/ICATAS2019/download.pdf:pdf},
journal = {Dep Comput Sci Univ Essex Colchester UK Tech Rep CSM404},
number = {August 2014},
pages = {1--23},
title = {{Multiagent Reinforcement Learning for Multi-Robot Systems : A Survey}},
year = {2004}
}
@inproceedings{Shi2006,
author = {Shi, Hongyan and Sun, Xiaoming and Sun, Changzhi and Chen, Dongyang and An, Yuejun},
booktitle = {Proceedings - ISDA 2006: Sixth International Conference on Intelligent Systems Design and Applications},
doi = {10.1109/ISDA.2006.38},
file = {:C$\backslash$:/Users/cairo/Downloads/complexity.pdf:pdf},
isbn = {0769525288},
keywords = {Autonomous mobile robot,Chaos,Path planning,masters,path planning},
mendeley-tags = {masters,path planning},
pages = {216--219},
title = {{Research of the path planning complexity for autonomous mobile robot under dynamic environments}},
volume = {3},
year = {2006}
}
@article{Mnih2015,
abstract = {The theory of reinforcement learning provides a normative account 1 , deeply rooted in psychological 2 and neuroscientific 3 perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems 4,5 , the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopa-minergic neurons and temporal difference reinforcement learning algorithms 3. While reinforcement learning agents have achieved some successes in a variety of domains 6-8 , their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks 9-11 to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games 12. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks. We set out to create a single algorithm that would be able to develop a wide range of competencies on a varied range of challenging tasks-a central goal of general artificial intelligence 13 that has eluded previous efforts 8,14,15. To achieve this, we developed a novel agent, a deep Q-network (DQN), which is able to combine reinforcement learning with a class of artificial neural network 16 known as deep neural networks. Notably, recent advances in deep neural networks 9-11 , in which several layers of nodes are used to build up progressively more abstract representations of the data, have made it possible for artificial neural networks to learn concepts such as object categories directly from raw sensory data. We use one particularly successful architecture, the deep convolutional network 17 , which uses hierarchical layers of tiled convolutional filters to mimic the effects of receptive fields-inspired by Hubel and Wiesel's seminal work on feedforward processing in early visual cortex 18-thereby exploiting the local spatial correlations present in images, and building in robustness to natural transformations such as changes of viewpoint or scale. We consider tasks in which the agent interacts with an environment through a sequence of observations, actions and rewards. The goal of the agent is to select actions in a fashion that maximizes cumulative future reward. More formally, we use a deep convolutional neural network to approximate the optimal action-value function Q {\~{A}} s,a ð Þ{\~{}} max p r t zcr tz1 zc 2 r tz2 z. .. js t {\~{}}s, a t {\~{}}a, p {\^{A}} {\~{A}} , which is the maximum sum of rewards r t discounted by c at each time-step t, achievable by a behaviour policy p 5 P(ajs), after making an observation (s) and taking an action (a) (see Methods) 19. Reinforcement learning is known to be unstable or even to diverge when a nonlinear function approximator such as a neural network is used to represent the action-value (also known as Q) function 20. This instability has several causes: the correlations present in the sequence of observations, the fact that small updates to Q may significantly change the policy and therefore change the data distribution, and the correlations between the action-values (Q) and the target values rzc max a 0 Q s 0 , a 0 ð Þ. We address these instabilities with a novel variant of Q-learning, which uses two key ideas. First, we used a biologically inspired mechanism termed experience replay 21-23 that randomizes over the data, thereby removing correlations in the observation sequence and smoothing over changes in the data distribution (see below for details). Second, we used an iterative update that adjusts the action-values (Q) towards target values that are only periodically updated, thereby reducing correlations with the target. While other stable methods exist for training neural networks in the reinforcement learning setting, such as neural fitted Q-iteration 24 , these methods involve the repeated training of networks de novo on hundreds of iterations. Consequently, these methods, unlike our algorithm, are too inefficient to be used successfully with large neural networks. We parameterize an approximate value function Q(s,a;h i) using the deep convolutional neural network shown in Fig. 1, in which h i are the parameters (that is, weights) of the Q-network at iteration i. To perform experience replay we store the agent's experiences e t 5 (s t ,a t ,r t ,s t 1 1) at each time-step t in a data set D t 5 {\{}e 1 ,{\ldots},e t {\}}. During learning, we apply Q-learning updates, on samples (or minibatches) of experience (s,a,r,s9) , U(D), drawn uniformly at random from the pool of stored samples. The Q-learning update at iteration i uses the following loss function:},
author = {Mnih, Volodymyr and Kavukcuoglu, Koray and Silver, David and Rusu, Andrei A and Veness, Joel and Bellemare, Marc G and Graves, Alex and Riedmiller, Martin and Fidjeland, Andreas K and Ostrovski, Georg and Petersen, Stig and Beattie, Charles and Sadik, Amir and Antonoglou, Ioannis and King, Helen and Kumaran, Dharshan and Wierstra, Daan and Legg, Shane and Hassabis, Demis},
doi = {10.1038/nature14236},
file = {:C$\backslash$:/Users/cairo/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Mnih et al. - 2015 - Human-level control through deep reinforcement learning.pdf:pdf},
journal = {Nature},
keywords = {dqn,drl,masters},
mendeley-tags = {dqn,drl,masters},
month = {feb},
pages = {529},
publisher = {Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
title = {{Human-level control through deep reinforcement learning}},
url = {https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf https://doi.org/10.1038/nature14236 http://10.0.4.14/nature14236 https://www.nature.com/articles/nature14236{\#}supplementary-information},
volume = {518},
year = {2015}
}
@inproceedings{Wu2012,
abstract = {We present a system to classify the gesture from only one learning example. The inputs are duo-modality, i.e. RGB and depth sensor from Kinect. Our system performs morphological denoising on depth images and automatically segments the temporal boundaries. Features are extracted based on Extended-Motion-History-Image (Extended-MHI) and the Multi-view Spectral Embedding (MSE) algorithm is used to fuse duo modalities in a physically meaningful manner. Our approach achieves less than 0.3 in Levenshtein distance in CHALEARN Gesture Challenge validation batches 1.},
author = {Wu, Di and Zhu, Fan and Shao, Ling},
booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops},
doi = {10.1109/CVPRW.2012.6239179},
file = {:C$\backslash$:/Users/cairo/Downloads/fifi.pdf:pdf},
isbn = {9781467316118},
issn = {21607508},
keywords = {one shot,sycut},
mendeley-tags = {one shot,sycut},
month = {jun},
pages = {7--12},
publisher = {IEEE},
title = {{One shot learning gesture recognition from RGBD images}},
url = {http://ieeexplore.ieee.org/document/6239179/},
year = {2012}
}
@article{Wurman2008,
abstract = {The Kiva warehouse-management system creates a new paradigm for pick-pack-and-ship warehouses that significantly improves worker productivity. The Kiva system uses movable storage shelves that can be lifted by small, autonomous robots. By bringing the product to the worker, productivity is increased by a factor of two or more, while simultaneously improving accountability and flexibility. A Kiva installation for a large distribution center may require 500 or more vehicles. As such, the Kiva system represents the first commercially available, large-scale autonomous robot system. The first permanent installation of a Kiva system was deployed in the summer of 2006.},
archivePrefix = {arXiv},
arxivId = {http://dx.doi.org/10.1108/BIJ-10-2012-0068},
author = {Wurman, Peter R. and D'Andrea, Raffaello and Mountz, Mick},
doi = {10.1609/aimag.v29i1.2082},
eprint = {/dx.doi.org/10.1108/BIJ-10-2012-0068},
file = {:C$\backslash$:/Users/cairo/Downloads/2082-Article Text-2255-1-10-20080325.pdf:pdf},
isbn = {9781577353232},
issn = {0738-4602},
journal = {AI Magazine},
keywords = {Deployed Innovative Applications of Artificial Int,Technical Papers,master,multi robot},
mendeley-tags = {master,multi robot},
number = {1},
pages = {9--20},
pmid = {42012058},
primaryClass = {http:},
title = {{Coordinating Hundreds of Cooperative, Autonomous Vehicles in Warehouses}},
volume = {29},
year = {2008}
}
@incollection{Khamis2015,
abstract = {Multi-robot systems (MRS) are a group of robots that are designed aiming to perform some collective behavior. By this collective behavior, some goals that are impossible for a single robot to achieve become feasible and attainable. There are several foreseen benefits of MRS compared to single robot systems such as the increased ability to resolve task complexity, increasing performance, reliability and simplicity in design. These benefits have attracted many researchers from academia and industry to investigate how to design and develop robust versatile MRS by solving a number of challenging problems such as complex task allocation, group formation, cooperative object detection and tracking, communication relaying and self-organization to name just a few. One of the most challenging problems of MRS is how to optimally assign a set of robots to a set of tasks in such a way that optimizes the overall system performance subject to a set of constraints. This problem is known as Multi-robot Task Allocation (MRTA) problem. MRTA is a complex problem especially when it comes to heterogeneous unreliable robots equipped with different capabilities that are required to perform various tasks with different requirements and constraints in an optimal way. This chapter provides a comprehensive review on challenging aspects of MRTA problem, recent approaches to tackle this problem and the future directions.},
address = {Cham},
author = {Khamis, Alaa and Hussein, Ahmed and Elmogy, Ahmed},
booktitle = {Cooperative Robots and Sensor Networks 2015},
doi = {10.1007/978-3-319-18299-5_2},
editor = {Koub{\^{a}}a, Anis and {Mart{\'{i}}nez-de Dios}, J.Ramiro},
file = {:C$\backslash$:/Users/cairo/Documents/F5F5/Thesis/utmthesis-master/odd{\_}citation/paper{\_}published.pdf:pdf},
isbn = {978-3-319-18299-5},
number = {May},
pages = {31--51},
publisher = {Springer International Publishing},
title = {{Multi-robot Task Allocation: A Review of the State-of-the-Art}},
url = {http://link.springer.com/10.1007/978-3-642-55029-4},
volume = {554},
year = {2015}
}
@article{Yu2016,
abstract = {We study the problem of optimal multi-robot path planning on graphs MPP over four distinct minimization objectives: the makespan (last arrival time), the maximum (single-robot traveled) distance, the total arrival time, and the total distance. In a related paper, we show that these objectives are distinct and NP-hard to optimize. In this work, we focus on efficiently algorithmic solutions for solving these optimal MPP problems. Toward this goal, we first establish a one-to-one solution mapping between MPP and network-flow. Based on this equivalence and integer linear programming (ILP), we design novel and complete algorithms for optimizing over each of the four objectives. In particular, our exact algorithm for computing optimal makespan solutions is a first such that is capable of solving extremely challenging problems with robot-vertex ratio as high as 100{\%}. Then, we further improve the computational performance of these exact algorithms through the introduction of principled heuristics, at the expense of some optimality loss. The combination of ILP model based algorithms and the heuristics proves to be highly effective, allowing the computation of 1.x-optimal solutions for problems containing hundreds of robots, densely populated in the environment, often in just seconds.},
archivePrefix = {arXiv},
arxivId = {1507.03290},
author = {Yu, Jingjin and LaValle, Steven M.},
doi = {10.1109/TRO.2016.2593448},
eprint = {1507.03290},
file = {:C$\backslash$:/Users/cairo/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Yu, LaValle - 2016 - Optimal Multirobot Path Planning on Graphs Complete Algorithms and Effective Heuristics.pdf:pdf},
isbn = {9781577356158},
issn = {15523098},
journal = {IEEE Transactions on Robotics},
keywords = {masters,multi robot,review},
mendeley-tags = {masters,multi robot,review},
number = {5},
pages = {1163--1177},
title = {{Optimal Multirobot Path Planning on Graphs: Complete Algorithms and Effective Heuristics}},
url = {https://arxiv.org/pdf/1507.03290.pdf},
volume = {32},
year = {2016}
}
@article{Kala2009,
abstract = {The pace of development and automation urge the need of robots controlling much of the work which used to be done mainly by humans. The modern technology has emphasized on the need to move a robot in an environment which is dynamically changing. An example of such an application may be the use of robots in industry to carry tools and other materials from one place to other. Since many robots would be working together, we need to ensure a collision free navigation plan for each of the robots. In this paper we find out the nearly most optimal path of the robot using Genetic, ANN and A* algorithms at each instant of time of robot travel. It may be used by the industry to send robots for surveys, data acquisition, doing specific work etc. The collision free movement of robot in a moving obstacle environment can be used to move robot in a world of robots. Results show that all 3 algorithms are able to move the robot without any collisions.},
author = {Kala, Rahul and Shukla, Anupam and Tiwari, Ritu and Rungta, Sourabh and Janghel, R.R.},
doi = {10.1109/CSIE.2009.854},
file = {:C$\backslash$:/Users/cairo/Downloads/CSIE-2009.pdf:pdf},
isbn = {978-0-7695-3507-4},
journal = {2009 WRI World Congress on Computer Science and Information Engineering},
keywords = {A*,masters},
mendeley-tags = {A*,masters},
number = {January},
pages = {705--713},
title = {{Mobile Robot Navigation Control in Moving Obstacle Environment Using Genetic Algorithm, Artificial Neural Networks and A* Algorithm}},
url = {http://ieeexplore.ieee.org/document/5171088/},
year = {2009}
}
@inproceedings{NIPS2012_4824,
abstract = {We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSRVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5{\%} and 17.0{\%} which is considerably better than the previous state of the art. The neural network, which has 60 million paramters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolutional operation. To reduce overfitting in the fully-connected layers, we employed a recently-developed method called 'dropout' that proved to be effective. We also entered a variant of the model in the ILSVRC-2012 competition and achievd a top-5 test error rate of 15.3{\%}, compared to 26.2{\%} achieved by the second-best entry.},
archivePrefix = {arXiv},
arxivId = {1102.0183},
author = {Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E},
booktitle = {Advances in Neural Information Processing Systems 25},
doi = {http://dx.doi.org/10.1016/j.protcy.2014.09.007},
editor = {Pereira, F and Burges, C J C and Bottou, L and Weinberger, K Q},
eprint = {1102.0183},
file = {:C$\backslash$:/Users/cairo/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Krizhevsky, Hinton - 2012 - ImageNet Classification with Deep Convolutional Neural Networks.pdf:pdf},
isbn = {9781627480031},
issn = {10495258},
keywords = {sycut},
mendeley-tags = {sycut},
pages = {1097--1105},
pmid = {7491034},
publisher = {Curran Associates, Inc.},
title = {{ImageNet Classification with Deep Convolutional Neural Networks}},
url = {http://code.google.com/p/cuda-convnet/ http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf},
volume = {25},
year = {2012}
}
@article{STRENS2004,
author = {Strens, Malcolm and Windelinckx, Neil},
doi = {10.1007/978-3-540-32274-0_17},
file = {:C$\backslash$:/Users/cairo/Downloads/10.1007{\_}978-3-540-32274-0{\_}17.bib:bib;:C$\backslash$:/Users/cairo/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Strens, Windelinckx - 2005 - Combining Planning with Reinforcement Learning for Multi-robot Task Allocation.pdf:pdf},
journal = {Adaptive Agents and Multi-Agent Systems II. AAMAS 2004, AAMAS 2003. Lecture Notes in Computer Science},
pages = {260--274},
publisher = {Springer, Berlin, Heidelberg},
title = {{Combining Planning with Reinforcement Learning for Multi-robot Task Allocation}},
url = {http://link.springer.com/10.1007/978-3-540-32274-0{\_}17},
volume = {3394},
year = {2005}
}
@inproceedings{Yakovlev2015,
abstract = {{\textcopyright} Springer International Publishing Switzerland 2015.Square grids are commonly used in robotics and game development as spatial models and well known in AI community heuristic search algorithms (such as A*, JPS, Theta* etc.) are widely used for path planning on grids. A lot of research is concentrated on finding the shortest (in geometrical sense) paths while in many applications finding smooth paths (rather than the shortest ones but containing sharp turns) is preferable. In this paper we study the problem of generating smooth paths and concentrate on angle constrained path planning. We put angle-constrained path planning problem formally and present a new algorithm tailored to solve it - LIAN. We examine LIAN both theoretically and empirically. We show that it is sound and complete (under some restrictions). We also show that LIAN outperforms the analogues when solving numerous path planning tasks within urban outdoor navigation scenarios.},
author = {Yakovlev, Konstantin and Baskin, Egor and Hramoin, Ivan},
booktitle = {KI 2015: Advances in Artificial Intelligence - 38th Annual German Conference on AI, Dresden, Germany, September 21-25, 2015, Proceedings},
doi = {10.1007/978-3-319-24489-1_16},
file = {:C$\backslash$:/Users/cairo/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Yakovlev, Baskin, Hramoin - Unknown - Grid-based angle-constrained path planning.pdf:pdf},
isbn = {9783319244884},
issn = {16113349},
keywords = {A*,Angle constrained paths,Grid worlds,Grids,Heuristic search,LIAN,Path finding,Path planning,Theta*,masters,path planning},
mendeley-tags = {masters,path planning},
pages = {208--221},
title = {{Grid-based angle-constrained path planning}},
url = {http://dx.doi.org/10.1007/978-3-},
volume = {9324},
year = {2015}
}
@book{Shiller2015,
abstract = {The sense of touch allows humans and higher animals to perform coordinated and efficient interactions within their environment. Recently, tactile sensor arrays providing high force, spatial, and temporal resolution became available for robotics, which allows us to consider new control strategies to exploit this important and valuable sensory channel for grasping and manipulation tasks. Successful dexterous manipulation strongly depends on tight feedback loops integrating proprioceptive, visual, and tactile feedback.We introduce a framework for tactile servoing that can realize specific tactile interaction patterns, for example to establish and maintain contact (grasping) or to explore and manipulate objects. We demonstrate and evaluate the capabilities of the proposed control framework in a series of preliminary experiments employing a 16 × 16 tactile sensor array attached to a Kuka LWR arm as a large fingertip. {\textcopyright} Springer International Publishing Switzerland 2015.},
author = {Shiller, Zvi},
doi = {10.1007/978-3-319-14705-5},
file = {:C$\backslash$:/Users/cairo/Downloads/9783319147048-c2.pdf:pdf},
isbn = {978-3-319-14704-8},
issn = {22110992},
keywords = {masters,motion planning,online planning,path planning,trajectrory optimization},
mendeley-tags = {masters,path planning},
title = {{Motion and Operation Planning of Robotic Systems}},
url = {http://link.springer.com/10.1007/978-3-319-14705-5},
volume = {29},
year = {2015}
}
@inproceedings{Sharifahzannierahsyedmarzuki2012,
author = {Sharifah zannierah syed marzuki and Zaidatul nadiah abu yazid and Nor khairunnisa mat yunus and Ida rosnita ismail},
booktitle = {KONFERENSI AKADEMIK (KONAKA) UNIVERSITI TEKNOLOGI MARA PAHANG MALAYSIA, At Bukit Gambang Resort Pahang Malaysia},
file = {:C$\backslash$:/Users/cairo/Downloads/TheAnatomyofHalalSlaughtering9October2012.pdf:pdf},
number = {December 2012},
title = {{The Anatomy of Halal Slaughtering : Issues and Challenges The Anatomy of Halal Slaughtering : Issues and Challenges}},
url = {https://www.researchgate.net/publication/306380068{\%}0AThe},
year = {2012}
}
@book{Thrun2005,
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Thrun, Sebastian and BURGARD, Wolfram and FOX, Dieter},
booktitle = {The MIT Press; Intelligent Robotics and Autonomous Agents series edition},
doi = {10.1145/504729.504754},
eprint = {arXiv:1011.1669v3},
file = {:C$\backslash$:/Users/cairo/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Thrun, Burgard, Fox - 1999 - PROBABILISTIC ROBOTICS.pdf:pdf},
isbn = {9788578110796},
issn = {00010782},
keywords = {masters},
mendeley-tags = {masters},
pmid = {25246403},
title = {{PROBABILISTIC ROBOTICS}},
url = {https://docs.ufpr.br/{~}danielsantos/ProbabilisticRobotics.pdf http://www.academia.edu/download/32378515/ProbabilisticRobotics.pdf{\%}0Ahttps://docs.ufpr.br/{~}danielsantos/ProbabilisticRobotics.pdf},
year = {2005}
}
@article{Francesca2014,
abstract = {We introduce AutoMoDe: a novel approach to the automatic design of control software for robot swarms. The core idea in AutoMoDe recalls the approach commonly adopted in machine learning for dealing with the bias--variance tradedoff: to obtain suitably general solutions with low variance, an appropriate design bias is injected. AutoMoDe produces robot control software by selecting, instantiating, and combining preexisting parametric modules---the injected bias. The resulting control software is a probabilistic finite state machine in which the topology, the transition rules and the values of the parameters are obtained automatically via an optimization process that maximizes a task-specific objective function. As a proof of concept, we define AutoMoDe-Vanilla, which is a specialization of AutoMoDe for the e-puck robot. We use AutoMoDe-Vanilla to design the robot control software for two different tasks: aggregation and foraging. The results show that the control software produced by AutoMoDe-Vanilla (i) yields good results, (ii) appears to be robust to the so called reality gap, and (iii) is naturally human-readable.},
author = {Francesca, Gianpiero and Brambilla, Manuele and Brutschy, Arne and Trianni, Vito and Birattari, Mauro},
doi = {10.1007/s11721-014-0092-4},
file = {:C$\backslash$:/Users/cairo/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Francesca et al. - 2014 - AutoMoDe A novel approach to the automatic design of control software for robot swarms.pdf:pdf},
isbn = {1172101400924},
issn = {19353820},
journal = {Swarm Intelligence},
keywords = {AutoMoDe,Automatic design,Evolutionary robotics,Swarm robotics,masters,swarm},
mendeley-tags = {masters,swarm},
month = {jun},
number = {2},
pages = {89--112},
publisher = {Springer US},
title = {{AutoMoDe: A novel approach to the automatic design of control software for robot swarms}},
url = {http://link.springer.com/10.1007/s11721-014-0092-4},
volume = {8},
year = {2014}
}
@article{Bayindir2016,
abstract = {Swarm intelligence principles have been widely studied and applied to a number of different tasks where a group of autonomous robots is used to solve a problem with a distributed approach, i.e. without central coordination. A survey of such tasks is presented, illustrating various algorithms that have been usfile:///C:/Users/CAIRO-PC/Documents/F5F5/swam robotics/p17.pdfed to tackle the challenges imposed by each task. Aggregation, flocking, foraging, object clustering and sorting, navigation, path formation, deployment, collaborative manipulation and task allocation problems are described in detail, and a high-level overview is provided for other swarm robotics tasks. For each of the main tasks, (1) swarm design methods are identified, (2) past works are divided in task-specific categories, and (3) mathematical models and performance metrics are described. Consistently with the swarm intelligence paradigm, the main focus is on studies characterized by distributed control, simplicity of individual robots and locality of sensing and communication. Distributed algorithms are shown to bring cooperation between agents, obtained in various forms and often without explicitly programming a cooperative behavior in the single robot controllers. Offline and online learning approaches are described, and some examples of past works utilizing these approaches are reviewed.},
author = {Bayindir, Levent},
doi = {10.1016/j.neucom.2015.05.116},
file = {:C$\backslash$:/Users/cairo/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Bayindir - 2016 - A review of swarm robotics tasks.pdf:pdf},
isbn = {0925-2312},
issn = {18728286},
journal = {Neurocomputing},
keywords = {Cooperation,Distributed task,Swarm robotics,masters,review paper,swarm},
mendeley-tags = {masters,review paper,swarm},
month = {jan},
pages = {292--321},
publisher = {Elsevier},
title = {{A review of swarm robotics tasks}},
url = {https://www.sciencedirect.com/science/article/pii/S0925231215010486},
volume = {172},
year = {2016}
}
@inproceedings{Gu2017,
abstract = {Reinforcement learning holds the promise of enabling autonomous robots to learn large repertoires of behavioral skills with minimal human intervention. However, robotic applications of reinforcement learning often compromise the autonomy of the learning process in favor of achieving training times that are practical for real physical systems. This typically involves introducing hand-engineered policy representations and human-supplied demonstrations. Deep reinforcement learning alleviates this limitation by training general-purpose neural network policies, but applications of direct deep reinforcement learning algorithms have so far been restricted to simulated settings and relatively simple tasks, due to their apparent high sample complexity. In this paper, we demonstrate that a recent deep reinforcement learning algorithm based on offpolicy training of deep Q-functions can scale to complex 3D manipulation tasks and can learn deep neural network policies efﬁciently enough to train on real physical robots. We demonstrate that the training times can be further reduced by parallelizing the algorithm across multiple robots which pool their policy updates asynchronously. Our experimental evaluation shows that our method can learn a variety of 3D manipulation skills in simulation and a complex door opening skill on real robots without any prior demonstrations or manually designed representations.},
author = {Gu, Shixiang and Holly, Ethan and Lillicrap, Timothy and Levine, Sergey},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
file = {:C$\backslash$:/Users/cairo/Downloads/ieee drl .pdf:pdf},
isbn = {9781509046324},
keywords = {Learning and Adaptive Systems,drl,masters},
mendeley-tags = {drl,masters},
pages = {3389--3396},
title = {{Deep reinforcement learning for robotic manipulation with Asynchronous Off-Policy Updates}},
url = {http://arxiv.org/abs/1610.00633},
year = {2017}
}
@article{Tuci2018,
abstract = {In recent years, there has been a growing interest in designing multi-robot systems (hereafter MRSs) to provide cost effective, fault-tolerant and reliable solutions to a variety of automated applications. Here, we review recent advancements in MRSs specifically designed for cooperative object transport, which requires the members of MRSs to coordinate their actions to transport objects from a starting position to a final destination. To achieve cooperative object transport, a wide range of transport, coordination and control strategies have been proposed. Our goal is to provide a comprehensive summary for this relatively heterogeneous and fast-growing body of scientific literature. While distilling the information, we purposefully avoid using hierarchical dichotomies, which have been traditionally used in the field of MRSs. Instead, we employ a coarse-grain approach by classifying each study based on the transport strategy used; pushing-only, grasping and caging. We identify key design constraints that may be shared among these studies despite considerable differences in their design methods. In the end, we discuss several open challenges and possible directions for future work to improve the performance of the current MRSs. Overall, we hope to increase the visibility and accessibility of the excellent studies in the field and provide a framework that helps the reader to navigate through them more effectively.},
author = {Tuci, Elio and Alkilabi, Muhanad H. M. and Akanyeti, Otar},
doi = {10.3389/frobt.2018.00059},
file = {:C$\backslash$:/Users/cairo/Downloads/mrs.pdf:pdf},
issn = {2296-9144},
journal = {Frontiers in Robotics and AI},
keywords = {caging,cooperative object transport,multi-robot systems,pulling,pushing},
number = {May},
title = {{Cooperative Object Transport in Multi-Robot Systems: A Review of the State-of-the-Art}},
url = {https://www.frontiersin.org/article/10.3389/frobt.2018.00059/full},
volume = {5},
year = {2018}
}
@inproceedings{Koenig2002,
address = {Washington,DC},
author = {Koenig, Sven and Likhachev, Maxim},
booktitle = {The 2002 IEEE International Conference on Robotics {\&} Automation},
doi = {10.1109/ROBOT.2002.1013481},
file = {:C$\backslash$:/Users/cairo/Downloads/replanning.pdf:pdf},
isbn = {0780372727},
keywords = {masters,replanning},
mendeley-tags = {masters,replanning},
number = {May},
pages = {968--975},
title = {{Improved Fast Replanning for Robot Navigation in Unknown Terrain}},
year = {2002}
}
@article{Erdmann1986,
author = {Erdmann, M A and Lozano-Perez, T},
doi = {10.1109/ROBOT.1986.1087401},
file = {:C$\backslash$:/Users/cairo/Downloads/decoupled.pdf:pdf},
journal = {Algorithmica},
number = {4},
pages = {477--521},
title = {{On multiple moving objects}},
volume = {2},
year = {1986}
}
@article{DBLP:journals/corr/SchulmanWDRK17,
abstract = {We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a "surrogate" objective function using stochastic gradient ascent. Whereas standard policy gradient methods perform one gradient update per data sample, we propose a novel objective function that enables multiple epochs of minibatch updates. The new methods, which we call proximal policy optimization (PPO), have some of the benefits of trust region policy optimization (TRPO), but they are much simpler to implement, more general, and have better sample complexity (empirically). Our experiments test PPO on a collection of benchmark tasks, including simulated robotic locomotion and Atari game playing, and we show that PPO outperforms other online policy gradient methods, and overall strikes a favorable balance between sample complexity, simplicity, and wall-time.},
archivePrefix = {arXiv},
arxivId = {1707.06347},
author = {Schulman, John and Wolski, Filip and Dhariwal, Prafulla and Radford, Alec and Openai, Oleg Klimov and Klimov, Oleg},
eprint = {1707.06347},
file = {:C$\backslash$:/Users/cairo/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Schulman et al. - Unknown - Proximal Policy Optimization Algorithms(2).pdf:pdf;:C$\backslash$:/Users/cairo/Downloads/SchulmanWDRK17.bib:bib},
journal = {CoRR},
keywords = {DRL,PPO,master,masters},
mendeley-tags = {DRL,PPO,master,masters},
title = {{Proximal Policy Optimization Algorithms}},
url = {http://arxiv.org/abs/1707.06347 https://arxiv.org/pdf/1707.06347.pdf},
volume = {abs/1707.0},
year = {2017}
}
@article{Chandia2018,
author = {Chandia, Mahmood and Soon, Jan Mei},
doi = {10.1108/BFJ-03-2017-0129},
file = {:C$\backslash$:/Users/cairo/Downloads/MahmoodandSoon{\_}Thevariationsinreligiousandlegalunderstandingsonhalalslaughter.pdf:pdf},
issn = {0007070X},
journal = {British Food Journal},
keywords = {Halal,Industry practices,Law,Poultry,Reversible,Slaughter,Stunning},
number = {3},
pages = {714--730},
title = {{The variations in religious and legal understandings on halal slaughter}},
volume = {120},
year = {2018}
}
@article{Zhang2017,
abstract = {A multi-robot tractor system for conducting agriculture field work was developed in order to reduce total work time and to improve work efficiency. The robot tractors can form a spatial pattern, I-pattern, V-pattern or W-pattern, during the work process. The safety zones of each robot were defined as a circle and a rectangle. The robots can coordinate to turn to the next lands without collision or deadlock. The efficiency of the system depends on the number of robots, the spatial pattern, the setting distance between each robot, and the field length. Three simulations were carried out to determine the usefulness of the system. The simulation results showed that the efficiency range of three robots using the I-pattern is from 83.2{\%} to 89.8{\%} at a field length of 100 m. The efficiency range of seven robots using the W-pattern is from 59.4{\%} to 65.8{\%} at a field length of 100 m. However, the minimum efficiency of seven robots using the W-pattern is 84.9{\%} at a field length of 500 m. The efficiency would be higher than 85{\%} if the field length was larger than 500 m. Thus, the newly developed multi-robot tractor system is more effective in a large field.},
author = {Zhang, Chi and Noguchi, Noboru},
doi = {10.1016/j.compag.2017.08.017},
file = {:C$\backslash$:/Users/cairo/Downloads/agriculture.pdf:pdf;:C$\backslash$:/Users/cairo/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Zhang, Noguchi - 2017 - Development of a multi-robot tractor system for agriculture field work.pdf:pdf},
issn = {01681699},
journal = {Computers and Electronics in Agriculture},
keywords = {Formation control,Headland turn coordination,Safety zone,Work efficiency,agriculture,multi robot},
mendeley-tags = {agriculture,multi robot},
month = {nov},
pages = {79--90},
title = {{Development of a multi-robot tractor system for agriculture field work}},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0168169917303381},
volume = {142},
year = {2017}
}
@article{Dolgov2007,
abstract = {Abstract-Although the problem of determining the minimum cost path through a graph arises naturally in a number of interesting applications, there has been no underlying theory to guide the development of efficient search procedures. Moreover, there is no adequate conceptual framework within which the various ad hoc search strategies proposed to date can be compared. This paper describes how heuristic information from the problem domain can be incorporated into a formal mathematical theory of graph searching and demonstrates an optimality property of a class of search strate- gies.},
author = {Dolgov, Dmitri and Thrun, Sebastian and Montemerlo, Michael and Diebel, James},
file = {:C$\backslash$:/Users/cairo/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Dolgov et al. - 2007 - Practical Search Techniques in Path Planning for Autonomous Driving.pdf:pdf},
journal = {Compare A Journal Of Comparative Education},
number = {2},
pages = {32--37},
title = {{Practical Search Techniques in Path Planning for Autonomous Driving}},
year = {2007}
}
@article{Otsu1979,
abstract = {A nonparametric and unsupervised method ofautoma- tic threshold selection for picture segmentation is presented. An optimal threshold is selected by the discriminant criterion, namely, so as to maximize the separability of the resultant classes in gray levels. The procedure is very simple, utilizing only the zeroth- and the first-order cumulative moments of the gray-level histogram. It is straightforward to extend the method to multithreshold problems. Several experimental results are also presented to support the validity of the method.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Otsu, Nobuyuki},
doi = {10.1109/TSMC.1979.4310076},
eprint = {arXiv:1011.1669v3},
file = {:C$\backslash$:/Users/cairo/Downloads/04310076.pdf:pdf},
isbn = {0018-9472},
issn = {0018-9472},
journal = {IEEE Transactions on Systems, Man, and Cybernetics},
number = {1},
pages = {62--66},
pmid = {4310076},
title = {{A Threshold Selection Method from Gray-Level Histograms}},
url = {http://ieeexplore.ieee.org/document/4310076/},
volume = {9},
year = {1979}
}
@inproceedings{6696520,
abstract = {From exploring planets to cleaning homes, the reach and versatility of robotics is vast. The integration of actuation, sensing and control makes robotics systems powerful, but complicates their simulation. This paper introduces a versatile, scalable, yet powerful general-purpose robot simulation framework called V-REP. The paper discusses the utility of a portable and flexible simulation framework that allows for direct incorporation of various control techniques. This renders simulations and simulation models more accessible to a general-public, by reducing the simulation model deployment complexity. It also increases productivity by offering built-in and ready-to-use functionalities, as well as a multitude of programming approaches. This allows for a multitude of applications including rapid algorithm development, system verification, rapid prototyping, and deployment for cases such as safety/remote monitoring, training and education, hardware control, and factory automation simulation.},
author = {Rohmer, E and Singh, S P N and Freese, M},
booktitle = {2013 IEEE/RSJ International Conference on Intelligent Robots and Systems},
doi = {10.1109/IROS.2013.6696520},
issn = {2153-0858},
keywords = {control engineering computing,digital simulation,masters,r,simulation},
mendeley-tags = {masters,simulation},
month = {nov},
pages = {1321--1326},
title = {{V-REP: A versatile and scalable robot simulation framework}},
year = {2013}
}
@article{Morley2015,
abstract = {The Academic Phrasebank is a general resource for academic writers. It aims to provide the phraseological ‘nuts and bolts' of academic writing organised according to the main sections of a research paper or dissertation. Other phrases are listed under the more general communicative functions of academic writing. The resource was designed primarily for academic and scientific writers who are non-native speakers of English. However, native writers may still find much of the material helpful. In fact, recent data suggests that the majority of users are native speakers of English. The phrases, and the headings under which they are listed, can be used simply to assist you in thinking about the content and organisation of your own writing, or the phrases can be incorporated into your writing where this is appropriate. In most cases, a certain amount of creativity and adaptation will be necessary when a phrase is used.},
author = {Morley, John},
doi = {10.1093/ml/gcm073},
file = {:C$\backslash$:/Users/cairo/Downloads/phrase book.pdf:pdf},
isbn = {0754655210},
issn = {0027-4224},
journal = {The University of Manchester},
keywords = {academic writers,resource,scientific,theoretical},
pages = {106},
title = {{Academic phrasebank: A compendium of commonly used phrasal elements in academic English in PDF format 2014b edition}},
year = {2015}
}
@book{Tanembaum2003,
author = {Tanembaum, As},
booktitle = {Amsterdam: Vrije Universiteit},
file = {:C$\backslash$:/Users/cairo/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Andrew Tanenbaum - Unknown - Computer Networks.pdf:pdf},
isbn = {0130661023},
keywords = {masters},
mendeley-tags = {masters},
title = {{Computer Networks}},
url = {https://theswissbay.ch/pdf/Gentoomen Library/Networking/Prentice Hall - Computer Networks Tanenbaum 4ed.pdf http://scholar.google.com/scholar?hl=en{\&}btnG=Search{\&}q=intitle:Redes+de+computadores+Quarta+edi??o{\#}7},
year = {2003}
}
@article{Opfer2011,
author = {Opfer, Stephan and Skubch, Hendrik and Geihs, Kurt},
file = {:C$\backslash$:/Users/cairo/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Opfer, Skubch, Geihs - Unknown - Cooperative Path Planning for Multi-Robot Systems in Dynamic Domains.pdf:pdf},
isbn = {978-953-307-842-7},
journal = {Cdn.Intechopen.Com},
keywords = {masters,multi robot,path planning},
mendeley-tags = {masters,multi robot,path planning},
pages = {237--258},
title = {{Cooperative Path Planning for Multi-Robot Systems in Dynamic Domains}},
url = {http://cdn.intechopen.com/pdfs/24661/InTech-Cooperative{\_}path{\_}planning{\_}for{\_}multi{\_}robot{\_}systems{\_}in{\_}dynamic{\_}domains.pdf},
year = {2011}
}
@article{Vinyals2016,
abstract = {Learning from a few examples remains a key challenge in machine learning. Despite recent advances in important domains such as vision and language, the standard supervised deep learning paradigm does not offer a satisfactory solution for learning new concepts rapidly from little data. In this work, we employ ideas from metric learning based on deep neural features and from recent advances that augment neural networks with external memories. Our framework learns a network that maps a small labelled support set and an unlabelled example to its label, obviating the need for fine-tuning to adapt to new class types. We then define one-shot learning problems on vision (using Omniglot, ImageNet) and language tasks. Our algorithm improves one-shot accuracy on ImageNet from 87.6{\%} to 93.2{\%} and from 88.0{\%} to 93.8{\%} on Omniglot compared to competing approaches. We also demonstrate the usefulness of the same model on language modeling by introducing a one-shot task on the Penn Treebank.},
archivePrefix = {arXiv},
arxivId = {1606.04080},
author = {Vinyals, Oriol and Blundell, Charles and Lillicrap, Timothy and Kavukcuoglu, Koray and Wierstra, Daan},
doi = {10.1109/CVPR.2016.95},
eprint = {1606.04080},
file = {:C$\backslash$:/Users/cairo/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Vinyals et al. - Unknown - Matching Networks for One Shot Learning.pdf:pdf},
isbn = {9781467369640},
issn = {10636919},
keywords = {one shot,sycut},
mendeley-tags = {one shot,sycut},
pmid = {24920543},
title = {{Matching Networks for One Shot Learning}},
url = {https://arxiv.org/pdf/1606.04080.pdf http://arxiv.org/abs/1606.04080},
year = {2016}
}
@inproceedings{Liekna2014,
abstract = {Swarm robotics is a relatively new research area inspired from biological systems such as ant or bee colonies. It composes a system consisting of many small robots with simple control mechanisms capable of achieving complex collective behaviours on the swarm level such as aggregation, pattern formation and collective transportation to name a few. However, more research is still required to apply swarm robotics in practice. Within the scope of our knowledge at the moment there are no swarm robotics applications for real-life problems. The current research tends to solve specific tasks in controlled laboratory environments. In this paper we survey the existing works on swarm robotics and their applications and also analyse the potential of their applicability to solve real-life problems.},
author = {Liekna, Aleksis and Grundspenkis, Janis},
booktitle = {Engineering for Rural Development},
issn = {16915976},
keywords = {Overview,Practical applications,Swarm robotics,Tasks of the swarm,masters,review paper,swarm},
mendeley-tags = {masters,review paper,swarm},
pages = {271--277},
title = {{Towards practical application of swarm robotics: Overview of swarm tasks}},
volume = {13},
year = {2014}
}
@inproceedings{Goto2003,
author = {Goto, Takayuki and Kosaka, Takeshi and Noborio, Hiroshi},
booktitle = {Proceedings of the 2003 IEEWRSJ InU. Conference on Intelligent Robots and Systems Las Vegas, Nevada},
file = {:C$\backslash$:/Users/cairo/Downloads/euclidean.pdf:pdf},
isbn = {0780378601},
keywords = {A*,masters},
mendeley-tags = {A*,masters},
title = {{On the Heuristics of A* or A Algorithm in ITS and Robot Path-Planning}},
year = {2003}
}


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@inproceedings{Bergner2014b,
 abstract = {Challenges of visualization and clustering are explored with respect to sequence data from a simulation-based assessment task. Visualization issues include representing progress towards a goal and accounting for variable-length sequences. Clustering issues focus on external criteria with respect to official scoring rubrics of the same sequence data. The analysis has a confirmatory flavor; the goal is to understand to what extent clustering solutions align with score categories. It is found that choices related to data preprocessing, distance metric and external cluster validity measures all impact agreement between cluster assignments and scores. This work raises key issues about clustering of educational data, especially in the presence of multidimensionality. Different clustering protocols may lead to different solutions, no one of which is uniquely best.},
 author = {Bergner, Yoav and Shu, Zhan and von Davier, Alina A.},
 booktitle = {Proceedings of 7th International Conference on Educational Data Mining},
 file = {:Users/bergner/Documents/Mendeley Desktop/Bergner, Shu, von Davier - 2014 - Visualization and Confirmatory Clustering of Sequence Data from a Simulation-Based Assessment Task.pdf:pdf},
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 pages = {177--184},
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@InProceedings{bradley12,
  title = {Sample Complexity of Composite Likelihood},
  author = {Joseph Bradley and Carlos Guestrin},
  pages = {136--160},
  abstract = {We present the first PAC bounds for learning parameters of Conditional Random Fields (CRFs) with general structures over discrete and real-valued variables. Our bounds apply to composite likelihood, which generalizes maximum likelihood and pseudolikelihood. Moreover, we show that the only existing algorithm with a PAC bound for learning high-treewidth discrete models can be viewed as a computationally inefficient method for computing pseudolikelihood. We present an extensive empirical study of the statistical efficiency of these estimators, as predicted by our bounds. Finally, we use our bounds to show how to construct computationally and statistically efficient composite likelihood estimators.},
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@inproceedings{hldvt2016,
  author    = {Kasper S{\o}e Luckow and
               Corina S. Pasareanu},
  title     = {Log2model: inferring behavioral models from log data},
  booktitle = {{IEEE} International High Level Design Validation and Test Workshop,
               {HLDVT} 2016, Santa Cruz, CA, USA, October 7-8, 2016},
  pages     = {25--29},
  year      = {2016},
  crossref  = {DBLP:conf/hldvt/2016},
  url       = {http://doi.ieeecomputersociety.org/10.1109/HLDVT.2016.7748251},
  doi       = {10.1109/HLDVT.2016.7748251},
  timestamp = {Fri, 25 Nov 2016 14:45:09 +0100},
  biburl    = {http://dblp.uni-trier.de/rec/bib/conf/hldvt/LuckowP16},
  bibsource = {dblp computer science bibliography, http://dblp.org}
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    journal = {PLoS Biology},
    month = {December}
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@article{DeepUrfoldexplain,
	title = {Explainable Deep Generative Models, Ancestral Fragments, and Murky Regions of the Protein Structure Universe},
	author = {Eli J. Draizen and Cameron Mura and Philip E. Bourne},
	year = {2022},
	journal = {bioRxiv},
    html = {https://doi.org/10.1101/2022.11.16.516787},
    poster = {https://doi.org/10.5281/zenodo.7312514},
    abbr = {MLSB 2022},
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@article{DeepUrfold,
    abbr = {preprint},
	title = {Deep Generative Models of Protein Structure Uncover Distant Relationships Across a Continuous Fold Space},
	author = {Eli J. Draizen and Stella Veretnik and Cameron Mura and Philip E. Bourne},
	year = {2022},
	journal = {bioRxiv},
    html = {https://doi.org/10.1101/2022.07.29.501943},
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@article{scs2018,
    author = {Daniele Parisi* and Gabriel J. Olguín-Orellana* and Eli J. Draizen* and ... and R. Gonzalo Parra},
    title = {Nurturing tomorrow's leaders: The ISCB Student Council Symposia in 2018},
    journal = {F1000Research},
    year = {2019},
    doi = {10.12688/f1000research.17739.1},
    html = {http://dx.doi.org/doi:10.12688/f1000research.17739.1}
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@article{evcouplings,
    author = {Thomas A. Hopf and ... and Eli J. Draizen and ... and Chris Sander and Debora S. Marks},
    title = {The {EV}couplings {P}ython framework for coevolutionary sequence analysis},
    journal = {Bioinformatics},
    year = {2018},
    doi = {10.1101/326918},
    html = {http://dx.doi.org/10.1101/326918}
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@article{jaiswal2020,
    abbr = {preprint},
    title={Deep Learning of Protein Structural Classes: Any Evidence for an 'Urfold'?},
    author={Menuka Jaiswal and Saad Saleem and Yonghyeon Kweon and Eli J. Draizen and Stella Veretnik and Cameron Mura and Philip E. Bourne},
    year={2020},
    journal={arXiv},
    arxiv = {2005.08443}
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@article{mullane2019,
    abbr = {preprint},
    title={Machine Learning for Classification of Protein Helix Capping Motifs},
    author={Sean Mullane and Ruoyan Chen and Sri Vaishnavi Vemulapalli and Eli J. Draizen and Ke Wang and Cameron Mura and Philip E. Bourne},
    year={2019},
    journal={arXiv},
    arxiv = {1905.00455}
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@article{cosb,
    author = {Cameron Mura* and Eli J. Draizen* and Philip E. Bourne},
    title = {Structural Biology Meets Data Science--Does Anything Change?},
    journal = {Current Opinion in Structural Biology},
    year = {2018},
    abstract = {https://doi.org/10.1016/j.sbi.2018.09.003},
    arxiv = {1807.09247}

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@article{carbobuilder,
    author = {Nigel W. Moriarty and Eli J. Draizen and Paul D. Adams},
    title = {A restraints editor for generation and customisation of geometry restraints},
    journal = {Acta Cryst. D},
    year = "2017",
    volume = "73",
    number = "2",
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    month = "Feb",
    doi = {10.1107/S2059798316016570},
    html = {https://doi.org/10.1107/S2059798316016570}
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    volume = {57},
    number = {1},
    pages = {245-262},
    doi = {10.1146/annurev-pharmtox-010716-104659},
    html = {https://doi.org/10.1146/annurev-pharmtox-010716-104659}
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    title = {Histone{DB} 2.0: a histone database with variants -- an integrated resource to explore histones and their variants},
    journal = {Database},
    volume = {2016},
    number = {},
    pages = {baw014},
    year = {2016},
    doi = {10.1093/database/baw014},
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