Abstract
Artificial intelligence technology has brought tremendous changes to human life and production methods. Mobile robots, UAVs, and autonomous driving technology have gradually entered people’s daily life. As a typical issue for a mobile robot, the planning of an optimal mobile path is very important, especially in the military and emergency rescue. In order to ensure the efficiency of operation and the accuracy of the path, it is crucial for the robot to find the optimal path quickly and accurately. This paper discusses a new method and MP-SAPSO algorithm for addressing the issue of path planning based on the PSO algorithm by combining particle swarm optimization (PSO) algorithm with the simulated annealing (SA) algorithm and mutation particle and adjusting the parameters. The MP-SAPSO algorithm improves the accuracy of path planning and the efficiency of robot operation. The experiment also demonstrates that the MP-SAPSO algorithm can be used to effectively address path planning issue of mobile robots.
Highlights
Aiming at the shortcomings of premature convergence of traditional particle swarm optimization, this paper incorporates the concept of population mutation factor in simulated annealing algorithm (SA) and genetic algorithm [16], assembles the optimization for adaptive annealing particle swarm optimization algorithm, and adjusts the original parameters and application parameters in order to apply to the actual path planning application
In terms of convergence speed, the two strategies are faster than the traditional particle swarm optimization (PSO), the application of convergence factor makes the convergence speed not limited by the number of iterations, resulting in the fact that mutation particles cannot directly reflect the role, and the curve presents the trend of premature convergence
In order to reflect the superiority of the MP-swarm optimization algorithm based on simulated annealing (SAPSO) algorithm over the traditional PSO algorithm, we apply them to the same environmental model for path planning and solution and analyze the results
Summary
Compared to other algorithms, there are problems such as premature convergence and more likely to fall into local optimal solutions To address these shortcomings [2], this paper combines particle swarm optimization algorithm and simulated annealing algorithm, introduces the concept of mutation factor, and proposes a new robot path planning scheme for path planning under a known environment model. Roberge et al combine genetic algorithm and particle swarm optimization algorithm to solve the path planning problem of UAV in a complex three-dimensional environment [4]. Zeng et al aimed to propose a particle swarm optimization (PSO) algorithm based on nonhomogeneous Markov chain and differential evolution for path planning of intelligent robot when encountering obstacles in the environment [11]. It is suitable to be used as a path optimization algorithm for mobile robot
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