A new generation of swarming robots which can independently learn and evolve new behaviours in the wild is one step closer, hints new research from the University of Bristol and the University of the West of England.

The team used artificial evolution to enable the robots to automatically learn ‘swarm’ behaviours, which are understandable to humans.

The University of Bristol said this new advance in Advanced Intelligent Systems, could create new robotic possibilities for environmental monitoring, disaster recovery, infrastructure maintenance, logistics and agriculture.

Until now, artificial evolution has typically been run on a computer which is external to the robotic swarm, with the best strategy then copied to the robots, it said.

By using a custom-made swarm of robots with high-processing power embedded within the swarm, the Bristol team were able to discover which rules give rise to desired swarm behaviours.

“Human-understandable controllers allow us to analyse and verify automatic designs, to ensure safety for deployment in real-world applications,” explained lead author Simon Jones, from the University of Bristol’s Robotics Lab.

Dr. Hauert, Senior Lecturer in Robotics in the Department of Engineering Mathematics and Bristol Robotics Laboratory (BRL), said the news is the first step towards robot swarms that “automatically discover suitable swarm strategies in the wild”.

“The next step will be to get these robot swarms out of the lab and demonstrate our proposed approach in real-world applications,” she said.

The University of Bristol said that in future, starting from scratch, a robot swarm could discover a suitable strategy directly in situ, and change the strategy when the swarm task, or environment changes.

"In many modern AI systems, especially those that employ Deep Learning, it is almost impossible to understand why the system made a particular decision,” explained Professor Alan Winfield, BRL and Science Communication Unit, UWE.

“This lack of transparency can be a real problem if the system makes a bad decision and causes harm. An important advantage of the system described in this paper is that it is transparent: its decision making process is understandable by humans."