Based on the mobile robot dynamic characteristics of the working environment, this paper presents a model based on classic artificial potential field potential field on the basis of considering dynamic model of velocity potential field, using quantum particle swarm optimization algorithm, using quantum particle swarm optimization algorithm for rapid global search and artificial potential field operations, easy real-time control of quickness, realize the defects of optimal path. Simulation results show that this method can effectively improve the performance of path planning.
Recently, mobile robotics is increasingly becoming responsive to many areas including agriculture, health, nanotechnology, nuclear technologies and the military. The sensitive aspect of these applications requires collision - free path planning performances of the mobile robot. Since path planning is treated as an optimization problem, we provide in this paper, a comparison study between two path planning approaches with obstacles avoidance: A classic method is addressed by the deterministic Levenberg Marquardt (LM) optimization technique and an evolutionary one is presented by the heuristic Particle Swarm Optimization (PSO) technique. Simulation results show main advantages and drawbacks of each approaches. Hence, PSO has been demonstrated to be more efficient and useful especially in dynamic navigation environments.
We show under some specific conditions that the formal diffusion approximation for the motion of a particle in a random velocity field is valid.