Adaptive A*/NSGA-II framework for multi-goal navigation of mobile robots
The autonomous execution of routine sequential tasks by mobile robots in obstacle-dense environments poses substantial challenges for long-duration operations, as it simultaneously demands energy efficiency, kinematically feasible trajectory generation, and collision-free maneuvering. Although genetic algorithms (GAs) have been successfully applied to such problems, they often struggle to converge toward a feasible solution space when initialized with random coordinates in complex multi-goal maps. To address these limitations, we propose a hierarchical path planning framework for multi-goal navigation. Population initialization is accelerated by integrating the deterministic seed path planning of A* with the multi-objective optimization capabilities of the Non-dominated Sorting GA II (NSGA-II). Subsequently, the following five conflicting criteria are simultaneously optimized over a B-Spline curve representation to ensure that the planned paths comply with the robot’s physical constraints: path length, cumulative wheel effort (energy consumption), trajectory curvature (smoothness), obstacle clearance (centering), and collision risk. We further introduce a softmin-based adaptive blending mechanism that automatically selects the most suitable path from the champion solutions on the Pareto front according to regional environmental conditions, thereby eliminating the need for manual user intervention. Extensive simulation results across five difficulty levels involving multiple segments demonstrate that the proposed adaptive NSGA-II method achieves path length improvements of 22%–42%, 8%–27%, and 16%–34% compared with Standard GA, ant-based whale optimization algorithm-artificial potential field, and HWPSO, respectively. The adaptive blending mechanism also attains the shortest mean path length in all five tested environments.