Mobile Robot Path Planning Based On Genetic Algorithm

Path planning for mobile robots is a complex problem that not only guarantees a collision-free path with minimum traveling distance but also requires smoothness and clearances. This dissertation presents a genetic algorithms approach for solving the path-planning problem in static and dynamic mobile robot environments. According to the characteristic of path planning problem, every component of the algorithms are analyzed carefully, including environment representation, chromosome representation, path evaluation, genetic operators design and GA parameters selection. A new hybrid genetic algorithm is presented by use of genetic algorithm to optimize the valid path generated by numerical potential method. Using the concept of auto control, a method adopting location feedback, direction predictive and path re-planning is used to deal with the problem in dynamic environments. This algorithm has to be tested against variety static or dynamic environments. The simulation experiments show that this algorithm is able to plan a better path rapidly in any complex environment only if the path exists, and validated the effectiveness of the proposed approach.