The Automated Fault-recovery for Four-legged Robots using Parallel Genetic Algorithm☆

Abstract It is expected that robots could operate autonomously in extreme environments without human intervention. However, it is not a trivial task to design robots robust to all kinds of faults. On the other hand, Biological entities have capability to create new behaviors overcoming unexpected damage on their body. This is also one of desirable properties for industrial robots operating remotely in extreme conditions. In this paper, we propose to use a bio- inspired learning algorithm to generate new behaviors on a four-legged robot against unexpected body damages. Since the learning algorithm can be accelerated using parallelism on multiple machines, it is possible to adapt to changes (damages) quickly using remote computational resources. Experimental results show that the robot could adapt to damages on different part of robot's body successfully.