Benchmark of swarm robotics distributed techniques in a search task

Micael S. Couceiro*, Patricia A. Vargas, Rui P. Rocha, Nuno M F Ferreira

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

81 Citations (Scopus)


This paper presents a survey on multi-robot search inspired by swarm intelligence by further classifying and discussing the theoretical advantages and disadvantages of the existing studies. Subsequently, the most attractive techniques are evaluated and compared by highlighting their most relevant features. This is motivated by the gradual growth of swarm robotics solutions in situations where conventional search cannot find a satisfactory solution. For instance, exhaustive multi-robot search techniques, such as sweeping the environment, allow for a better avoidance of local solutions but require 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 experiments conducted to benchmark five state-of-the-art algorithms for cooperative exploration tasks. The simulated experimental results show the superiority of the previously presented Robotic Darwinian Particle Swarm Optimization (RDPSO), evidencing that sociobiological inspiration is useful to meet the challenges of robotic applications that can be described as optimization problems (e.g., search and rescue). 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 converges to the optimal solution faster and more accurately than the other approaches without significantly increasing the computational demand, memory and communication complexity.

Original languageEnglish
Pages (from-to)200-213
Number of pages14
JournalRobotics and Autonomous Systems
Issue number2
Publication statusPublished - 1 Feb 2014


  • Benchmark
  • Performance analysis
  • Search tasks
  • Swarm robotics

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Software
  • General Mathematics


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