Onboard evolution of human-understandable behaviour trees for robot swarms

Swarm robotics, inspired by swarms in nature, has great potential. The resilience, scalability, robustness and redundancy of having many robots collectively perform tasks such as mapping, disaster recovery, pollution control, and cleaning make for a compelling vision. To achieve this, we need to design swarm robot systems to have a desired collective behaviour, but the design of controllers for the individual robots of a swarm such that this behaviour emerges from the interaction of the individual robots is difficult. Current solutions often use off-line automatic discovery by artificial evolution of robot controllers, which are then transferred into the swarm. This is problematic for two important reasons. Firstly, since there is the need for additional supporting infrastructure, both to evolve the new controllers and to communicate them to the swarm, the swarm is not self-sufficient. Secondly, the evolved controllers are often opaque and hard to understand, an important consideration for safety and explainability reasons. In this work we tackle both of these issues. We build a swarm of robots with very high computing performance using recently available mobile computation devices. This high performance allows us to move the evolutionary process, dependent on processing power for simulation, into the swarm. Because the computational power of the swarm grows with the size of the swarm, it is both autonomous and scalable. We use behaviour trees as the individual robot controller architecture. They are modular, hierarchical and human readable. By developing automatic tools to simplify large evolved trees, we can understand, explain, and even improve the evolved controllers. By moving the evolutionary process into the swarm, and by using understandable controllers, we make the swarm autonomous, scalable, and understandable, necessary steps towards their real-world deployment.