Abstract:

For the problem of that the particle easily falls into local optimum when avoiding obstacles and cannot walk forward, the method of retreating the position of the local optimal particle to its historical best position and searching the feasible and optimal location in the 8-neighborhood of this position was proposed. The position which is nearest to the target and is not a barrier could be found by this method, then the particle was moved to it. Meanwhile, the global optimal position of current generation particle swarm could be found, and the location of each particle was set for this position to continue iteration. In the experiments for obstacle avoidance in grid map, the fact was found that when encountering obstacles, the particles fell into local optimum by the traditional methods, but particles could successfully avoid obstacles and arrive the end by using the improved algorithm. The improved algorithm was introduced into the 3D visual simulation system, in large map with many obstacles scenarios, the probability of falling into local optimum of the particle was reached to 50% which resulted in routing failure. After adding a circular slope to the obstacles for the further improvement of the algorithm, the probability of successful path-finding of the particle was increased to 83%. The experimental results show that the search ability of the improved algorithm is increased and it can effectively plan path in a complex scene.