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An Evolutionary Approach to Designing Autonomous Planetary Rovers

Current methods used to address the problem of autonomous navigation in planetary rovers rely on computationally expensive algorithms and elaborate 3D sensing strategies. This paper presents a low complexity alternative based on an evolved neural controller using continuous-value infrared sensors. A unified framework is presented for developing such controllers in virtual planetary rovers, utilising the latest advances in parallel optimisation by way of island model evolution. Preliminary results are presented demonstrating that this approach is capable of producing successful navigation and obstacle avoidance behaviours, showing that equivalent or better results can be achieved relative to traditional genetic algorithms.