An Improved Artificial Potential Field Method with Distributed Representation and Scale-Invariant Path Planning

For autonomous navigation systems, effective path planning in complex environments is critical. The widely used artificial potential field (APF) method, though simple and intuitive, has limitations due to its reliance on an artificially set scaling factor that requires manual tuning for different environments, introducing additional challenges in parameter adjustment. To address these limitations, we propose a novel approach inspired by neuroscience that redefines the attractive and repulsive forces in APF through distributed representations, accompanied by an adaptive mechanism to fine-tune their impact. This method, called the Neuro-Receptive Field Planner (NRF), derives its name from the distributional nature of these forces, which resemble neural receptive fields. Through theoretical analysis and numerical simulations, we validate NRF’s ability to decouple parameters and enhance interpretability, thereby demonstrating its flexibility and effectiveness. In tests conducted across three static and one dynamic environments, NRF exhibited good path smoothness, effective obstacle avoidance, and consistent performance across different scales, achieving the lowest average coefficient of variation (CV = 0.007 0.033) across all metrics compared to baseline methods. This study provides new insights into autonomous navigation and highlights the potential of neuroscience-inspired frameworks to enhance the robustness and adaptability of intelligent systems.