A faster path planner using accelerated particle swarm optimization
Artificial Life and Robotics
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We propose a method of robot path planning in unknown environments based on particle swarm optimization in this paper. We firstly transform the problem of robot path planning into a minimization one, and then define the fitness of a particle based on the positions of the target and the obstacles in the environment. The positions of globally best particle in each iterative are selected, and reached by the robot in sequence. In addition, the environment is unknown for the robot due to the limit range of its sensor. The robot processor updates its information immediately once the environment changes. The optimal path is generated with this method when the robot reaches its target. We perform some simulations in different dynamic environments, and the results show that the robot reaches its target with colliding free obstacles.