First published 2 September 2013
Immune-Inspired Error Detection for Multiple Faulty Robots in Swarm Robotics
Huikeng Lau, Iain Bate, Jon Timmis
Error detection and recovery are important issues in swarm robotics research, as they are a means by which fault tolerance can be achieved. Our previous work has looked at error detection for single failures in a swarm robotics scenario with the Receptor Density Algorithm. Three modes of failure to the wheels of individual robots was investigated and comparable performance to other statistical methods was achieved. In this paper, we investigate the potential of extending this approach to a robot swarm with multiple faulty robots. Two experiements have been conducted: a swarm of ten robots with 1 to 8 faulty robots, and a swarm of 10 to 20 robots with varying number of faulty robots. Results from the experiments showed that the proposed approach is able to detect errors in multiple faulty robots. The results also suggest the need to further investigate other aspects of the robot swarm that can potentially affect the performance of detection such as the communication range.