A closer look at adaptation mechanisms in simulated environment-driven evolutionary swarm robotics

  Swarm robotics is a special case within the general field of robotics. The distributed nature makes it more resilient with no single point of failure. Considering the application in remote locations, the swarm needs to adapt autonomously to a priori unknown environmental conditions. A special branch of evolutionary robotics achieves online adaptation during runtime through embedding the evolutionary algorithm in the robot. A well studied algorithm to do that is mEDEA, minimal Environment-driven Distributed Evolutionary Adaptation, which has been shown to be able to maintain the swarms integrity in an abruptly changing environment. It’s completely decentralised nature and the lack of an explicit fitness function leaves only the environment to provide the driving force for the evolutionary process.

This research investigate several aspects of environment-driven adaptation in evolutionary swarm robotics and mEDEA in particular.
First, adding a relative-fitness measure to optimise the robots’ energy maintenance which leads to longer working robots while preserving the distributed nature of the algorithm. Investigating the impact of communication costs on the performance of mEDEA.
Secondly, exploring the influence the environment has on the emergence of behaviour and informing the choice of parameters for future experimentation.
Lastly, investigating the impact of lifetime learning in addition to the slow evolutionary adaptation process and demonstrating how the interaction between inheritance and learning influences the mechanism, as postulated over a century ago by Baldwin.

  • Dates:

    2013 to date

  • Qualification:

    Doctorate (PhD)

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