Adaptive Particle Swarm and Ant Colony Optimization Path Planning for Autonomous Robot Navigation - Essaadoui, Alami; Baba, Youssef; Hamed, Oussama; Hamlich, Mohamed; Guemimi, Chafik; EL Kebch, Ali (Journal of Robotics and Control (JRC), 2025)
Path planning in cluttered and uncertain environments remains a significant challenge in robotics, autonomous navigation, and logistics optimization. This paper proposes a novel Adaptive Hybrid PSO-ACO Planner, which synergistically combines Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) to compute efficient paths in grid-based environments with static obstacles. Unlike traditional fixed-phase hybrids, our approach features a dynamic switching strategy between PSO and ACO based on real-time convergence behavior, allowing the algorithm to maintain progress and escape local minima. Additionally, adaptive parameter tuning is integrated to enhance the balance between global exploration and local exploitation throughout the search. The switching logic is governed by two criteria: a stagnation threshold that triggers phase transitions and a progress-dependent adaptation mechanism that adjusts search intensities over time. PSO dominates the early search phase, rapidly exploring the solution space, while ACO refines promising paths through pheromone-guided optimization in later stages. The proposed planner also includes a path reconstruction module to ensure solution completeness and robustness. Experimental evaluations on grid-based environments demonstrate that the proposed method consistently achieves higher path quality and faster convergence compared to standalone PSO and ACO approaches. Quantitative results demonstrate notable improvements in path efficiency and overall success rate across a range of obstacle densities. These advancements establish the Adaptive Hybrid PSOACO Planner as a robust and efficient tool for real-time and practical deployment in autonomous robot navigation systems.