Adaptive Path Planning for Mobile Robots Using a Hybrid PRM–GA Optimization Approach

This study addresses the challenge of path planning in mobile robots, that requires efficient navigation in complex environments. Traditional approaches often struggle to meet the increasing demands of modern multi‐robot systems operating in dynamic environments. To address these limitations, this study proposes an improved path planning technique by combining the probabilistic roadmap (PRM) with the genetic algorithm (GA), forming a hybrid PRM–GA approach designed to optimize the routes of mobile robots. Experiments were carried out for scenarios involving 2, 3, and 9 robots to analyze the performance of the proposed method under increasing complexity. The proposed PRM–GA method was compared with widely used path planning algorithms including , Rapidly exploring random tree (RRT), and conventional PRM. Performance of each method was evaluated focusing on path efficiency and energy consumption. The enhanced fitness function within the GA evaluates robot paths based not only on distance but also on smoothness and turn count, promoting routes with fewer directional changes. The proposed PRM–GA method reduces robot energy consumption while improving navigation efficiency. Experimental results demonstrate that the PRM–GA hybrid method outperforms , RRT, and PRM by encouraging smoother paths with fewer turns, thereby enhancing the operational efficiency of multi‐robot systems. The effectiveness of the proposed approach highlights its potential for practical applications in sectors where efficient mobile robot navigation is essential.