A Novel Approach to Path Planning for Autonomous Mobile Robots

Path planning is considered as one of the core problems of autonomous mobile robots. Different approaches have been proposed with different levels of complexity, accuracy, and applicability. This paper presents a hybrid approach to the problem of path planning that can be used to find global optimal collision-free paths. This approach relies on combining potential field (PF) method and genetic algorithm (GA) which takes the strengths of both and overcomes their inherent limitations. In this integrated frame, the PF method is designed as a gradient-based searching strategy to exploit local optimal, and the GA is used to explore over the whole problem space. In this work, different implementing strategies are examined in different complexity scenarios. The conducted experiments show that global optimal paths can be achieved effectively using the proposed approach with a strategy of high diversity and memorization.