Abstract
Path planning is the core technology of mobile robot decision‐making and control and is also a research hotspot in the field of artificial intelligence. Aiming at the problems of slow response speed, long planning path, unsafe factors, and a large number of turns in the conventional path planning algorithm, an improved multiobjective genetic algorithm (IMGA) is proposed to solve static global path planning in this paper. The algorithm uses a heuristic median insertion method to establish the initial population, which improves the feasibility of the initial path and generates a multiobjective fitness function based on three indicators: path length, path security, and path energy consumption, to ensure the quality of the planned path. Then, the selection, crossover, and mutation operators are designed by using the layered method, the single‐point crossover method, and the eight‐neighborhood‐domain single‐point mutation method, respectively. Finally, the delete operation is added, to further ensure the efficient operation of the mobile robot. Simulation experiments in the grid environment show that the algorithm can improve the defects of the traditional genetic algorithm (GA), such as slow convergence speed and easy to fall into local optimum. Compared with GA, the optimal path length obtained by planning is shortened by 17%.
Highlights
A mobile robot is a kind of machine device which can perform work automatically [1]
The planning algorithm used in the path search is the core of the whole mobile robot path planning problem, and the selection of the algorithm determines the quality of the planning path [10]
The purpose is to improve the shortcomings of the traditional genetic algorithm by optimizing some genetic operations and quickly plan the shortest, collision-free, and less turning safe operation path of the mobile robot in a static grid environment based on multiple planning indexes, so as to solve the path planning problem with more complex environment and task requirements
Summary
A mobile robot is a kind of machine device which can perform work automatically [1]. It can accept the command of human beings and act according to the scheme made by artificial intelligence technology [2]. There are many effective solutions to the problem of mobile robot path planning, but with the continuous development of science and technology, the environment faced by path planning technology will become more complex [11], and the task requirements will become more stringent. The purpose is to improve the shortcomings of the traditional genetic algorithm by optimizing some genetic operations and quickly plan the shortest, collision-free, and less turning safe operation path of the mobile robot in a static grid environment based on multiple planning indexes, so as to solve the path planning problem with more complex environment and task requirements.
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