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<p>Swarm robotics systems (SRS) enable robots to coordinate like social organisms, achieving complex tasks through collective behavior that exceeds the capabilities of individual robots. This study proposes an evolutionary approach for the automatic design of heterogeneous controllers and sub-team compositions in robotic swarms performing a cooperative transport task, in contrast to conventional homogeneous SRS<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(_s\)</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mi>s</mi> <mrow /> </mmultiscripts> </math></EquationSource> </InlineEquation>. The proposed method evolves both controller parameters and their distribution across the swarm. Utilizing artificial neural networks and evolutionary strategies, the system dynamically adapts robot roles according to task complexity. Experimental results demonstrate improvements in performance over conventional approaches, highlighting the benefits of adaptive heterogeneity in swarm robotics.</p>

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Evolutionary design of group organization and controllers in heterogeneous robotic swarms

  • 期刊论文

  • 发表日期：

    2025年10月22日

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  • Asad Razzaq,
  • Toshiyuki Yasuda

摘要

Swarm robotics systems (SRS) enable robots to coordinate like social organisms, achieving complex tasks through collective behavior that exceeds the capabilities of individual robots. This study proposes an evolutionary approach for the automatic design of heterogeneous controllers and sub-team compositions in robotic swarms performing a cooperative transport task, in contrast to conventional homogeneous SRS \(_s\) s . The proposed method evolves both controller parameters and their distribution across the swarm. Utilizing artificial neural networks and evolutionary strategies, the system dynamically adapts robot roles according to task complexity. Experimental results demonstrate improvements in performance over conventional approaches, highlighting the benefits of adaptive heterogeneity in swarm robotics.