An Optimized Path Planning Technique in A Static Environment for Computer Vision Applications

Particle swarm optimization is a powerful and easy heuristic optimization technique that has proven effective in many complicated optimization situations. Its current application is in resolving the issue of mobile robot route planning. However, the PSO approach has numerous drawbacks, including a lengthy computation time, a sluggish rate of convergence, and a low likelihood of finding both local and global positions. As a result, path planning may require numerous rounds. In this work, we provide an advanced Particle Swarm Optimization (APSO) system based on a primary path planner that can effectively discover a virtual robot’s optimal path through static landscapes dotted with convex obstacles. The integration form of particle swarm optimization, accelerates convergence and produces more optimal outcomes with fewer iteration steps.