Abstract
Path planning, as the core of navigation control for mobile robots, has become the focus of research in the field of mobile robots. Various path planning algorithms have been recently proposed. In this paper, in view of the advantages and disadvantages of different path planning algorithms, a heuristic elastic particle swarm algorithm is proposed. Using the path planned by the A* algorithm in a large-scale grid for global guidance, the elastic particle swarm optimization algorithm uses a shrinking operation to determine the globally optimal path formed by locally optimal nodes so that the particles can converge to it rapidly. Furthermore, in the iterative process, the diversity of the particles is ensured by a rebound operation. Computer simulation and real experimental results show that the proposed algorithm not only overcomes the shortcomings of the A* algorithm, which cannot yield the shortest path, but also avoids the problem of failure to converge to the globally optimal path, owing to a lack of heuristic information. Additionally, the proposed algorithm maintains the simplicity and high efficiency of both the algorithms.
Highlights
Mobile robot path planning aims to allow a robot to identify a safe, collision-free path from a starting point to a target point in a given environment, for example, for intelligent security or industrial manufacturing [1,2,3]
Intelligent optimization algorithms, which are based on natural heuristics, are as follows: Neural network algorithms [11], genetic algorithms [12], ant colony algorithms [13], particle swarm optimization (PSO) [14,15,16], artificial bee colony algorithms [17,18], artificial potential field (APF) algorithms based on the virtual force field [19], the Voronoi graph method, and the tangent graph method, based on a cell structure [20,21]
To solve the problem of the PSO algorithm falling into a local extremum, owing to the lack of heuristic information, we propose a heuristic elastic PSO path planning algorithm
Summary
Mobile robot path planning aims to allow a robot to identify a safe, collision-free path from a starting point to a target point in a given environment, for example, for intelligent security or industrial manufacturing [1,2,3]. Mac et al [14] propose a global path planning method, for a moving robot, based on the optimization of multiple target particle groups in a chaotic environment. To further improve the robustness of the algorithm and efficiency of the path planning, the APF based on the tangent vector (TVAPF) was optimized by using the particle swarm algorithm. The chicken swarm algorithm is introduced into the algorithm to disturb the search for stagnant particles and the globally optimal solution is used to bring the disturbed particles closer to it, in the introduced equation These improvements enhance the planning performance relative to the standard algorithm to a certain extent, they do not completely solve the problems of path planning. The A* algorithm introduces a heuristic function to guide the direction of the search and ensure the integrity of the algorithm while improving the search efficiency
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