Benchmark of
Swarm Robotics Distributed Techniques in a Search Task
Robotics and
Autonomous Systems, 62(2), pp. 200-213, Elsevier, Feb. 2014. DOI dx.doi.org/10.1016/j.robot.2013.10.004
This paper presents a survey on multi-robot search inspired on swarm intelligence, thus classifying and discussing the theoretical advantages and disadvantages of the existing studies so far. Subsequently, the most attractive techniques are evaluated and compared by highlighting the most relevant features one should expect under such distributed exploration task. This is motivated by the gradual growth of swarm robotic solutions in situations where conventional search cannot find a satisfactory solution. For instance, exhaustive multi-robot search techniques, such as sweeping the environment, allow avoiding local solutions but requires too much time to find the optimal one. Moreover, such techniques tend to fail in finding targets within dynamic and unstructured environments. This paper presents simulated and real experiments conducted to benchmark five state-of-the-art algorithms on cooperative exploration tasks in unknown scenarios. The simulated experimental results show the superiority of the previously presented Robotic Darwinian Particle Swarm Optimization (RDPSO), evidencing that sociobiological inspiration can be useful to meet the challenges of robotic applications that can be described as optimization problems (e.g., search and rescue applications). Moreover, the RDPSO is further compared with the best performing algorithms within a population of 14 e-pucks. It is observed that the RDPSO algorithm provably converges to the optimal solution faster and more accurately than the other approaches without significantly increasing the computational demand, memory and communication complexity.
Keywords ─ Multi-robot search, swarm algorithms, benchmarking, swarm intelligence.
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@ARTICLE(Couceiro_et_al_14a,
AUTHOR = "M. S. Couceiro and P. A. Vargas and R. P. Rocha
and N. M. F. Ferreira",
TITLE = "Benchmark of Swarm Robotics Distributed Techniques in a Search Task",
JOURNAL = "Robotics and Autonomous Systems",
VOLUME = "62",
NUMBER = "2",
YEAR = "2014",
MONTH = "Feb.",
PAGES = "200-213"
)
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Copyright © 2014 Rui P. Rocha, Dep. of Electrical and
Computer Engineering,