Application of adaptive genetic algorithm in robot path planning - Hecheng SANG; Shuanjun SONG; Xupeng XING; Yuanyi MENG; Zhouqiang ZHANG; Mingwei TANG (Xi'an Gongcheng Daxue xuebao, 2021)
Aiming at the shortcomings of traditional genetic algorithm in path planning, such as infeasible paths, falling into local optimum, and too many turns, an improved adaptive genetic algorithm was proposed. The initial population was optimized by prior knowledge to get the initial population which did not include the intersection with obstacles; at the same time, crossover probability and mutation probability were designed to avoid the algorithm′s falling into the local optimal solution and to improve the convergence speed. Finally the path smoothness and the shortest path were introduced at the evaluation criteria in the fitness function to make the planned path more efficient. The simulation results show that compared with the basic algorithm, when the number of obstacles is 20, the path is reduced by 4.2%; when the number is 115, the path is reduced by 25.1%. With the increasing of obstacles, the path reduction percentage presents an upward trend, and the number of iterations and that of turning points in the path of the algorithm are better than those in the basic genetic algorithm.