Waypoint-Based Path Planning for Autonomous Robots with PSO - Faustino, Gustavo Manhenti; Silva, Mateus Coelho (Anais do Workshop Latinoamericano de Dependabilidade e Segurança em Sistemas Veiculares (SSV); 2025: Anais do II Workshop Latinoamericano de Dependabilidade e Segurança em Sistemas Veiculares, 2025)
Autonomous robots are essential for numerous applications requiring high precision and involving significant risks. However, planning and optimizing paths for a single robot in a dynamic environment remains a highly complex task. This challenge necessitates a balance between obstacle avoidance, distance optimization, and efficiency. Traditional strategies often utilize a wide range of path planning algorithms, such as local avoidance, A*, and ACO. Nevertheless, an approach employing waypoints and a “danger zone” in conjunction with a Particle Swarm Optimization (PSO) algorithm can be an effective method for path optimization. Here, we demonstrate that this path planning algorithm provides an efficient and rapid means to generate a path to a goal in an environment with a single obstacle. Through simulations conducted in IR-SIM, a Python-based robotics simulator, we show that a customized PSO, equipped with a multi-objective fitness function, effectively analyzes both intersections with an area around the obstacle (the “danger zone”) and the total path distance. This approach yields paths with over a 97% success rate in guiding the robot to its goal and exhibits relatively low convergence times. Our results illustrate PSO’s effectiveness as a path planning algorithm, highlighting its adaptability to various types of obstacles and positions within a 2D environment. This strategy represents an advancement in the use of heuristic algorithms for autonomous robot path planning, leading to a faster, less computationally demanding algorithm with a high success rate, capable of avoiding diverse obstacle types and easily adaptable to a broad range of single-robot environmental problems.