Hybrid Aco-pso for Robotic Path Planning

In the context of robotic path planning, a common strategy is to assume that the world is static and believed on heuristic approaches and obstacle avoidance to deal with changes in the environment. When planning is not considered for potential changes of the environment into account usually leads to poor performances. In this paper, we propose a hybrid ACO-PSO approach that exploits the traversability information to compute paths that minimize the expected travel time of the robot. Experimental results shows about changes in the environment so that path planning performance can be improved.