Intelligent Robot Path Planning Based On Improved PSO

With the constant development of robot technology, the robot needs to complete complex tasks more and more, the optimization problem of path planning has become one of the hot current research in the field of robotics. The robots from the starting point to the goal point movement in the process, in the environment with obstacle space, how to choose an optimal or near-optimal collision-free path is a critical path planning studies.Firstly, the traditional Particle swarm optimization(PSO) algorithm in-depth research, analysis of the particle swarm optimization algorithm such as premature convergence, easily falling into local optimum and particle cross-border and other shortcomings, we propose a nonlinear dynamic adjustment of inertia weight PSO, the particles in the iteration the latter has the ability to jump out of local optimum, relative improvement to the premature convergence problem of particle exists, the constraint method of particle iteration occurs easily during the initial stage of transgression and the boundary connection strategy, optimize the function application of Matlab7.0simulation software are tested on it, verify the feasibility of the algorithm; Secondly, according to the characteristics of the robot path planning actual working environment, the establishment of environmental models use the grid method, the grid method and PSO algorithm fusion, the introduction of safety and smoothness index based on the path length of the fitness function, establish fitness function dynamically adjust the path length, the traditional PSO algorithm, the classic PSO and improved PSO intelligent robot path planning conducted experiments comparing the simulation to verify the superiority of the improved algorithm; Finally, WIM-RR wheeled smart robot platform for improved PSO intelligent robot path planning practical application, has once again proven the effectiveness of the improved algorithm.