1. Geoff Nagy. Active Recruitment in Dynamic Teams of Heterogeneous Robots. Master's thesis, Department of Computer Science, University of Manitoba, Winnipeg, Canada, October 2016. Abstract:

   Using teams of autonomous, heterogeneous robots to operate in dangerous environments has a number of advantages. Among these are cost-effctiveness and the ability to spread out skills among team members. The nature of operating in dangerous domains means that the risk of loss is higher - teams will often lose members and must acquire new ones. In this work, I explore various recruitment strategies for the purpose of improving an existing framework for team management. My additions allow robots to more actively acquire new teams members and assign tasks among other robots on a team without the intervention of a team leader. I evaluate this framework in simulated post-disaster environments where the risk of robot loss is high and communications are often unreliable. My results show that in many scenarios, active recruitment strategies provide signicant performance benefits.

   
   

   @mastersthesis{nagyThesis,
   author = {Geoff Nagy},
   title = {Active Recruitment in Dynamic Teams of Heterogeneous Robots},
   school = {Department of Computer Science, University of Manitoba},
   year = {2016},
   address = {Winnipeg, Canada},
   month = {October},
   pdf = {http://aalab.cs.umanitoba.ca/%7eandersj/Publications/pdf/NagyThesis2016.pdf},
   abstract = {Using teams of autonomous, heterogeneous robots to operate in dangerous environments has a number of advantages. Among these are cost-effctiveness and the ability to spread out skills among team members. The nature of operating in dangerous domains means that the risk of loss is higher - teams will often lose members and must acquire new ones. In this work, I explore various recruitment strategies for the purpose of improving an existing framework for team management. My additions allow robots to more actively acquire new teams members and assign tasks among other robots on a team without the intervention of a team leader. I evaluate this framework in simulated post-disaster environments where the risk of robot loss is high and communications are often unreliable. My results show that in many scenarios, active recruitment strategies provide signicant performance benefits.} 
   }
   

1. Jacky Baltes, Jonathan Bagot, Soroush Sadeghnejad, John Anderson, and Chen-Hsien Hsu. Full-Body Motion Planning for Humanoid Robots using Rapidly Exploring Random Trees. KI - Künstliche Intelligenz, pp 1-11, 2016. Abstract:

   Humanoid robots with many degrees of freedom have an enormous range of possible motions. To be able to move in complex environments and dexterously manipulate objects, humanoid robots must be capable of creating and executing complex sequences of motions to accomplish their tasks. For soccer playing robots (e.g., the participants of RoboCup), the highly dynamic environment require real-time motion planning in spite of the enormous search space of possible motions. In this research, we propose a practical solution to the general movers problem in the context of motion planning for robots. The proposed robot motion planner uses a sample-based tree planner combined with an incremental simulator that models not only collisions, but also the dynamics of the motion. Thus it can ensure that the robot will be dynamically stable while executing the motion. The effectiveness of the robot motion planner is demonstrated both in simulation and on a real robot, using a variation of the Rapidly Exploring Random Tree (RRT) type of motion planner. The results of our empirical evaluation show that CONNECT works better than EXTEND versions of the RRT algorithms in simple domains, but that this advantage disappears in more obstacle-filled environments. The evaluation also shows that our motion planning system is able to find and execute complex motion plans for a small humanoid robot.

   
   

   @article{RRTKI16,
   author = {Jacky Baltes and Jonathan Bagot and Soroush Sadeghnejad and John Anderson and Chen-Hsien Hsu},
   title = {Full-Body Motion Planning for Humanoid Robots using Rapidly Exploring Random Trees},
   journal = {KI - K{\"u}nstliche Intelligenz},
   year = {2016},
   issn = {1610-1987},
   pages = {1--11},
   publisher = {Springer},
   doi = {10.1007/s13218-016-0450-z},
   pdf = {http://aalab.cs.umanitoba.ca/%7eandersj/Publications/pdf/RRTKI16.pdf},
   abstract = { Humanoid robots with many degrees of freedom have an enormous range of possible motions. To be able to move in complex environments and dexterously manipulate objects, humanoid robots must be capable of creating and executing complex sequences of motions to accomplish their tasks. For soccer playing robots (e.g., the participants of RoboCup), the highly dynamic environment require real-time motion planning in spite of the enormous search space of possible motions. In this research, we propose a practical solution to the general movers problem in the context of motion planning for robots. The proposed robot motion planner uses a sample-based tree planner combined with an incremental simulator that models not only collisions, but also the dynamics of the motion. Thus it can ensure that the robot will be dynamically stable while executing the motion. The effectiveness of the robot motion planner is demonstrated both in simulation and on a real robot, using a variation of the Rapidly Exploring Random Tree (RRT) type of motion planner. The results of our empirical evaluation show that CONNECT works better than EXTEND versions of the RRT algorithms in simple domains, but that this advantage disappears in more obstacle-filled environments. The evaluation also shows that our motion planning system is able to find and execute complex motion plans for a small humanoid robot. } 
   }
   




2. Jacky Baltes, Kuo-Yang Tu, Soroush Sadeghnejad, and John Anderson. HuroCup: Competition for Multi-Event Humanoid Robot Athletes. The Knowledge Engineering Review, FirstView:1-14, 8 2016. Abstract:

   This paper describes the motivation for the development of the HuroCup competition and follows the rule development from its inaugural competition from 2002 to 2015. The history of HuroCup is broken down into its growing phase (2002-2006), a time of explosive growth (2007-2011) and current times. This paper describes the main research focus of HuroCup, the multi-event humanoid robot competition: (a) active balancing, (b) complex motion planning, and (c) human-robot interaction and shows how the various HuroCup events relate to those research topics. This paper concludes with some medium- and long-term goals of the rule development for HuroCup.

   
   

   @article{HuroCupKEReview16,
   author = {Jacky Baltes and Kuo-Yang Tu and Soroush Sadeghnejad and John Anderson},
   title = {HuroCup: Competition for Multi-Event Humanoid Robot Athletes},
   journal = {The Knowledge Engineering Review},
   volume = {FirstView},
   month = {8},
   month = {8},
   year = {2016},
   issn = {1469-8005},
   pages = {1--14},
   publisher = {Cambridge University Press},
   doi = {10.1017/S0269888916000114},
   pdf = {http://aalab.cs.umanitoba.ca/%7eandersj/Publications/pdf/HuroCupKEReview.pdf},
   abstract = { This paper describes the motivation for the development of the HuroCup competition and follows the rule development from its inaugural competition from 2002 to 2015. The history of HuroCup is broken down into its growing phase (2002-2006), a time of explosive growth (2007-2011) and current times. This paper describes the main research focus of HuroCup, the multi-event humanoid robot competition: (a) active balancing, (b) complex motion planning, and (c) human-robot interaction and shows how the various HuroCup events relate to those research topics. This paper concludes with some medium- and long-term goals of the rule development for HuroCup. } 
   }
   

1. Geoff Nagy and John Anderson. Active Recruitment Mechanisms for Heterogeneous Robot Teams in Dangerous Environments. In Advances in Artificial Intelligence: 29th Canadian Conference on Artificial Intelligence, Victoria, British Columbia, pages 276-281, June 2016. Abstract:

   Using teams of autonomous, heterogeneous robots to operate in dangerous environments means increased cost-effectiveness and the ability to spread skills among team members. The high risk of loss in these domains is a challenge to team management. Teams must be able to recruit the help of other robots in the environment, while balancing searching with performing immediately useful work. This paper describes additions to a framework for dynamic team management in dangerous domains in order to support various levels of active search for useful agents while balancing useful work in the domain.

   
   

   @inproceedings{NagyCanAI16,
   author = {Geoff Nagy and John Anderson},
   title = {Active Recruitment Mechanisms for Heterogeneous Robot Teams in Dangerous Environments},
   booktitle = {Advances in Artificial Intelligence: 29th Canadian Conference on Artificial Intelligence},
   address = {Victoria, British Columbia},
   month = {June},
   pages = {276--281},
   pdf = {http://aalab.cs.umanitoba.ca/%7eandersj/Publications/pdf/NagyCanAI16.pdf},
   year = {2016},
   abstract = {Using teams of autonomous, heterogeneous robots to operate in dangerous environments means increased cost-effectiveness and the ability to spread skills among team members. The high risk of loss in these domains is a challenge to team management. Teams must be able to recruit the help of other robots in the environment, while balancing searching with performing immediately useful work. This paper describes additions to a framework for dynamic team management in dangerous domains in order to support various levels of active search for useful agents while balancing useful work in the domain.} 
   }
   

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