Evolving Behaviour Trees for Supervisory Control of Robot Swarms

 Supervisory control of swarms is essential to their deployment in real world scenarios to both monitor their operation and provide guidance. We explore mechanisms by which humans can provide supervisory control to swarms to improve their performance. Rather than have humans guess the correct form of supervisory control, we use artificial evolution to learn effective human-readable strategies to help improve human operators understanding and performance when controlling swarms. Behaviour trees are applied to represent human readable decision strategies which are produced through evolution. These strategies can be thoroughly tested and can provide knowledge to be used in the future in a variety of scenarios. A simulated set of scenarios are investigated where a swarm of robots have to explore varying environments and reach sets of objectives. Effective supervisory control strategies are evolved to explore each environment using different local swarm behaviours. The evolved behaviour trees are examined in detail alongside swarm simulations to enable clear understanding of the supervisory strategies. We conclude by identifying the strengths in accelerated testing and the benefits of this approach for scenario exploration and training of human operators.

Using machine learning to understand how we can supervise large numbers of robots.

A framework to simulate the supervision of swarms with an artificial agent which monitors and changes the behaviour of the swarm. Non-obvious evolved supervisory strategies to cover different sets of environments.