Evolution of a Functionally Diverse Swarm via a Novel Decentralised Quality-Diversity Algorithm
This addresses a problem in evolutionary robotics for swarm systems, offering a novel approach to enhance robustness and performance, though it appears incremental as it builds on existing algorithms.
The paper tackled the challenge of evolving functional diversity in robot swarms without requiring geographical isolation, by introducing a decentralized variant of MAP-Elites hybridized with mEDEA, and showed that the new method outperforms mEDEA in diversity, coverage, and precision for a token-gathering task.
The presence of functional diversity within a group has been demonstrated to lead to greater robustness, higher performance and increased problem-solving ability in a broad range of studies that includes insect groups, human groups and swarm robotics. Evolving group diversity however has proved challenging within Evolutionary Robotics, requiring reproductive isolation and careful attention to population size and selection mechanisms. To tackle this issue, we introduce a novel, decentralised, variant of the MAP-Elites illumination algorithm which is hybridised with a well-known distributed evolutionary algorithm (mEDEA). The algorithm simultaneously evolves multiple diverse behaviours for multiple robots, with respect to a simple token-gathering task. Each robot in the swarm maintains a local archive defined by two pre-specified functional traits which is shared with robots it come into contact with. We investigate four different strategies for sharing, exploiting and combining local archives and compare results to mEDEA. Experimental results show that in contrast to previous claims, it is possible to evolve a functionally diverse swarm without geographical isolation, and that the new method outperforms mEDEA in terms of the diversity, coverage and precision of the evolved swarm.