Mobile Robot Path Planning Research Based On Genetic Algorithm And Rough Set Theory

As a key technology in mobile robot research field, path planning is a guarantee that the mobile robot can move safely and accomplish the task successfully. According to the characteristics of static state global path planning, this thesis presents a mobile robot path planning algorithm based on rough sets theory and genetic algorithm, and afterwards, does some simulation to validate the performance of this algorithm. The main aspects presented in this paper comprise:1. This thesis introduces the concept of mobile robot path planning and the current research status and development tendency both at home and abroad, discusses several commonly used mobile robot path planning algorithms and presents the target and significance of this project.2. Based on the environmental model established by grid method, we derive the moving rules of mobile robot by using rough sets theory, and present a mobile robot path planning algorithm based on rough set theory and genetic algorithm.3. This thesis presents an initial population generation algorithm based on rough set reduction. By using of the rough set’s powerful ability of data reduction and decision generation, a series of preliminary optimized individuals are produced to generate the initial population, which makes the individual in initial population have a high fitness even at the very start, lay a good foundation for the subsequent genetic operations and improve the performance of the genetic algorithm.4. The optimal path was found in use of genetic algorithm. Afterwards, MATLAB software is applied to simulate this algorithm in several kinds of complicated degree different maps. Simulation results show the feasibility and high effectiveness of this algorithm in path planning for mobile robot.