A collective robotic architecture in search and rescue scenarios

Multi-robot systems (MRS) may be very useful on assisting humans in many distributed activities, especially for search and rescue (SaR) missions in hazardous scenarios. However, there is a lack of full distributed solutions, addressing the advantages and limitations along different aspects of team operation, like communication requirements or scalability. In this paper, the effects of distributed group configurations are studied and results are drawn from collective exploration and collective inspection tasks in SaR scenarios, with the aim of understanding the main tradeoffs, and distilling design guidelines of collective architectures. With this purpose, extensive simulation experiments of MRS in a SaR scenario were carried out.

[1]  Howie Choset,et al.  Coverage for robotics – A survey of recent results , 2001, Annals of Mathematics and Artificial Intelligence.

[2]  R. Rocha Building volumetric maps with cooperative mobile robots and useful information sharing : a distributed control approach based on entropy , 2006 .

[3]  N. Nathan Self and will , 1997 .

[4]  Mac Schwager,et al.  From Theory to Practice: Distributed Coverage Control Experiments with Groups of Robots , 2008, ISER.

[5]  Jesus Suarez,et al.  A survey of animal foraging for directed, persistent search by rescue robotics , 2011, 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics.

[6]  R. Siegwart,et al.  Chapter 1 Experience in System Design for Human-Robot Teaming in Urban Search & Rescue ? , 2012 .

[7]  Ralph L. Hollis,et al.  Contact sensor-based coverage of rectilinear environments , 1999, Proceedings of the 1999 IEEE International Symposium on Intelligent Control Intelligent Systems and Semiotics (Cat. No.99CH37014).

[8]  Maria Isabel Ribeiro Obstacle Avoidance , 2005 .

[9]  Raghuveer M. Rao,et al.  Darwinian Particle Swarm Optimization , 2005, IICAI.

[10]  Andrew Howard,et al.  Multi-robot Simultaneous Localization and Mapping using Particle Filters , 2005, Int. J. Robotics Res..

[11]  Alcherio Martinoli,et al.  Multi-robot learning with particle swarm optimization , 2006, AAMAS '06.

[12]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[13]  Noa Agmon,et al.  Constructing spanning trees for efficient multi-robot coverage , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[14]  Yi Guo,et al.  Collaborative Robots for Infrastructure Security Applications , 2007, Mobile Robots.

[15]  David Portugal,et al.  Decision methods for distributed multi-robot patrol , 2012, 2012 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR).

[16]  Peter C. Cheeseman,et al.  Estimating uncertain spatial relationships in robotics , 1986, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[17]  Ruggero Carli,et al.  Discrete Partitioning and Coverage Control for Gossiping Robots , 2010, IEEE Transactions on Robotics.

[18]  Paolo Fiorini,et al.  Search and Rescue Robotics , 2008, Springer Handbook of Robotics.

[19]  Micael S. Couceiro,et al.  A novel multi-robot exploration approach based on Particle Swarm Optimization algorithms , 2011, 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics.

[20]  Jin-Hui Zhu,et al.  Obstacle avoidance with multi-objective optimization by PSO in dynamic environment , 2005, 2005 International Conference on Machine Learning and Cybernetics.