Path Planning Based On Probabilistic Roadmap And Ant Colony Optimization

Path planning is a crucial task in robotics and autonomous systems,where the goal is to find an optimal path from the start to the end position while avoiding obstacles.However,path planning becomes even more challenging in complex multiple obstacle environments.This research proposes a method combining Probabilistic Roadmap(PRM)and Ant Colony Optimization(ACO)algorithms to overcome the limitations of traditional path planning methods in terms of finding an optimal and smooth path.The proposed approach involves three main steps.Firstly,a probabilistic roadmap is constructed to represent the configuration space of the environment.Secondly,ACO algorithm is used to find the optimal path between the start and goal points based on the pheromone trail and heuristic information.Thirdly,Bezier curves are used to smooth the path by adding control points to the path obtained from ACO.The contributions of this research are twofold.First,the proposed approach improves the efficiency and accuracy of the path planning process.Second,the use of Bezier curves reduces the number of turns in the generated path and enhances the path smoothness.The experimental results demonstrate the effectiveness of the proposed method in terms of path length,smoothness,and feasibility.Moreover,the approach demonstrates robustness against complex environments,making it suitable for real-world applications.It can be extended to various robotic systems,including autonomous vehicles,unmanned aerial vehicles,and mobile robots.