A sizing method for a multi-robot system

Addresses a fast method for optimizing and sizing a multi-robot system. To demonstrate the performance of such an approach, a complex heterogeneous system is considered. It is composed of two populations of robots having different but complementary abilities. It is shown that it is possible to find the optimum criteria to execute the mission and obtain the best probable solution It is based on the stochastic model of the Markov chains. The method is applied to a generic mission of multi-robot cooperation. The mission has also been simulated several thousand hundred times on a computer to compare the results and validate the model. It is also possible, with this model, to quickly predict the system evolution from different initial states and study the impact of the variation of some parameters on the global result. Some ongoing experimentations are also introduced.

[1]  Y. Dube,et al.  An Autonomous Mobile Robot , 1992, Singapore International Conference on Intelligent Control and Instrumentation [Proceedings 1992].

[2]  Luc Steels,et al.  Towards a theory of emergent functionality , 1991 .

[3]  A. Liegeois,et al.  Analysis and prediction of the behavior of one class of multiple foraging robots with the help of stochastic Petri nets , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[4]  Francesco Mondada,et al.  Understanding collective aggregation mechanisms: From probabilistic modelling to experiments with real robots , 1999, Robotics Auton. Syst..

[5]  E. Pagello,et al.  Intelligent multirobot systems performing cooperative tasks , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[6]  Ronald C. Arkin,et al.  Multiagent Mission Specification and Execution , 1997, Auton. Robots.

[7]  Rodney A. Brooks,et al.  Small planetary rovers , 1990, EEE International Workshop on Intelligent Robots and Systems, Towards a New Frontier of Applications.

[8]  F. Spitzer.  Principles Of Random Walk , 1966 .

[9]  Olivier Simonin,et al.  Markovian analysis of a heterogeneous system: application to a cooperation task for multiple consumer robots , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[10]  Ronald C. Arkin,et al.  An Behavior-based Robotics , 1998 .

[11]  Taylor L. Booth,et al.  Sequential machines and automata theory , 1967 .

[12]  Rodney A. Brooks,et al.  A Robust Layered Control Syste For A Mobile Robot , 2022 .