Mobile robot trajectory planning in an environment with obstacles based on a modified particle swarm optimization algorithm

This paper investigates the construction of an intelligent system for planning the trajectory of a mobile robot in an environment with obstacles. To solve this problem, the authors propose to use a modified particle swarm optimization (PSO) algorithm. The modification involves three aspects. The first aspect introduces two parameters as detectors to find a particle that cannot improve its personal optimum and global optimum in a predetermined number of successive iterations, and replaces it with a rebuilt one. The second changes the velocity constraint and thus increases the diversity of the population. The third introduces a variable parameter ω, which balances the global and local search abilities. This modification increases the diversity of the population by balancing the global and local search capabilities, and avoids stagnation and local optimization problems without losing the fast convergence property of PSO. Modeling and analysis of the obtained data allow us to conclude that the proposed modified PSO algorithm is effective for planning the trajectory of a mobile robot in an environment with obstacles.