The Application of an Adaptive Genetic Algorithm Based on Collision Detection in Path Planning of Mobile Robots

• Published in: Computational intelligence and neuroscience Vol. 2021; no. 1; p. 5536574
• Main Authors: Hao, Kun, Zhao, Jiale, Wang, Beibei, Liu, Yonglei, Wang, Chuanqi
• Format: Journal Article
• Language: English
• Published: New York Hindawi 2021; John Wiley & Sons, Inc
• Subjects: Adaptive algorithms; Algorithms; Barriers; Cloning; Convergence; Crossovers; Efficiency; Evolution & development; Game theory; Gene mapping; Genetic algorithms; Mathematical models; Mutation; Neural networks; Optimization; Path planning; Physiological psychology; Population; Robotics industry; Robots; Run time (computers); Smoothness; Weight
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2021/5536574

An adaptive genetic algorithm based on collision detection (AGACD) is proposed to solve the problems of the basic genetic algorithm in the field of path planning, such as low convergence path quality, many iterations required for convergence, and easily falling into the local optimal solution. First, this paper introduces the Delphi weight method to evaluate the weight of path length, path smoothness, and path safety in the fitness function, and a collision detection method is proposed to detect whether the planned path collides with obstacles. Then, the population initialization process is improved to reduce the program running time. After comprehensively considering the population diversity and the number of algorithm iterations, the traditional crossover operator and mutation operator are improved, and the adaptive crossover operator and adaptive mutation operator are proposed to avoid the local optimal solution. Finally, an optimization operator is proposed to improve the quality of convergent individuals through the second optimization of convergent individuals. The simulation results show that the adaptive genetic algorithm based on collision detection is not only suitable for simulation maps with various sizes and obstacle distributions but also has excellent performance, such as greatly reducing the running time of the algorithm program, and the adaptive genetic algorithm based on collision detection can effectively solve the problems of the basic genetic algorithm.