Mobile Robot Path Planning in Static Environments using Particle Swarm Optimization
This addresses path planning for mobile robots in static environments, but it is incremental as it applies an existing optimization method to a known problem.
The paper tackles mobile robot path planning in static environments by proposing a Particle Swarm Optimization-based algorithm to compute a shortest collision-free path, with functionality demonstrated via simulation results.
Motion planning is a key element of robotics since it empowers a robot to navigate autonomously. Particle Swarm Optimization is a simple, yet a very powerful optimization technique which has been effectively used in many complex multi-dimensional optimization problems. This paper proposes a path planning algorithm based on particle swarm optimization for computing a shortest collision-free path for a mobile robot in environments populated with static convex obstacles. The proposed algorithm finds the optimal path by performing random sampling on grid lines generated between the robot start and goal positions. Functionality of the proposed algorithm is illustrated via simulation results for different scenarios.