Using ALC-PSO algorithm with particle growing method path planning in dynamic environments

This paper proposes an optimal path planning algorithm for mobile robots based on Particle Swam Optimization with an Aging Leader and Challengers (ALC-PSO) and Rapidly-exploring Random Tree (RRT), it improved algorithm of ALC-PSO to imitate concept of RRT root node grown into goal point in path planning, and add Danger Degree Map to avoid obstacles, this method is not only overcome the drawback for particle swam optimization which is easy to fall into local optimization in robotic path planning and the basic Rapidly-exploring Random Tree path planning in avoiding the premature convergence problem, but also improve both of algorithm which can´t plan in dynamic environment. From the results of simulations, we show that this algorithm can improve the stability of RRT path planning in dynamic environment, and ensure that the path is almost optimal.