Mobile Robot Path Planning in Static Environments using Particle Swarm Optimization

TL;DR

This paper introduces a particle swarm optimization-based algorithm for autonomous mobile robot path planning in static environments, efficiently finding shortest collision-free paths through simulation in various scenarios.

Contribution

It presents a novel PSO-based path planning algorithm that uses grid sampling to compute optimal paths in static environments with convex obstacles.

Findings

Successfully finds shortest collision-free paths in simulations.

Effective in static environments with convex obstacles.

Demonstrates potential for autonomous navigation improvements.

Abstract

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.