Abstract This paper presents the Intelligent Obstacle Dynamics and Path Complexity System (IODPCS), an interaction-aware, measurement-driven navigation framework for energy-efficient mobile robot navigation in complex and dynamic environments. The key novelty of the proposed approach lies in embedding interaction-aware motion regulation directly within a unified fuzzy–potential-field control structure that simultaneously governs trajectory deformation and velocity adaptation. Rather than treating path planning and speed control as separate layers, IODPCS integrates obstacle dynamics, environmental complexity, and velocity-dependent energy cost within a single closed-loop decision formulation. A fuzzy logic–based decision core is coupled with artificial potential field guidance to generate coordinated steering and speed commands without reliance on sequential planning or heuristic switching. An offline calibration stage initializes control parameters, followed by online feedback-driven adaptation that enables continuous refinement of navigation behavior under changing interaction conditions. The framework is validated through extensive simulation studies and real-world experiments on a wheeled mobile robot platform and is compared against conventional navigation methods under identical conditions. Experimental results demonstrate improved path tracking accuracy, smoother trajectories, faster reaction to dynamic obstacles, and up to 25% reduction in energy consumption. These results confirm the robustness, adaptability, and scalability of IODPCS for autonomous navigation in uncertain and dynamic environments.
Al-Kamil et al. (Wed,) studied this question.