Abstract
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.
Highlights
With the development of the modern society, mobile robots have been widely used in shopping malls, factories, hospitals, and other public places [1, 2]
Rough the above analysis, this paper presents an adaptive genetic algorithm based on collision detection (AGACD) for mobile robot path planning. is paper makes the following innovations and contributions: (1) this paper proposes a collision detection method to detect whether the planned path collides with the obstacle grid. e collision detection method is suitable for all static grid maps
In terms of the crossover operator and mutation operator, an adaptive crossover operator and adaptive mutation operator are proposed in this paper. ese two adaptive operators can protect high-quality individuals to speed up the convergence rate of the algorithm and enhance the evolutionary potential of low-quality individuals to increase the searching ability of the algorithm
Summary
With the development of the modern society, mobile robots have been widely used in shopping malls, factories, hospitals, and other public places [1, 2]. Compared with the traditional search algorithm, the intelligent evolutionary algorithm is applicable to both small-scale maps with few obstacles and large-scale maps with many obstacles. The basic genetic algorithm (BGA) has some problems, such as low efficiency, low quality of convergent individuals, many iterations of convergence, and falling into the local optimal solution. To solve these problems, many scholars have performed much research on genetic algorithm. Rough the above analysis, this paper presents an adaptive genetic algorithm based on collision detection (AGACD) for mobile robot path planning. Preprocessing includes expanding the obstacles and equating the mobile robot to the mass point. e expansion size is the sum of the radius of the mobile robot and the reserved safe distance
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