Real-Time Local Navigation of Mobile Robots Using an Operative Critical Point Bug Algorithm

Abstract

Path planning is a key challenge for autonomous mobile robots operating in dynamic and unknown environments. To address this problem, this paper proposes a novel local navigation approach based on the Operative Critical Point Bug (OCPB) algorithm, which enables real-time obstacle avoidance in the presence of both static and moving objects. The proposed method allows the robot to continuously adjust its trajectory according to locally perceived environmental changes, without relying on global maps. By integrating geometric constraints of the robot and surrounding obstacles, the algorithm ensures safe navigation and reduces collision risk. Unlike traditional bug-based strategies, OCPB improves adaptability and minimizes unnecessary detours during motion. Experimental and simulation results demonstrate that the proposed approach achieves efficient, reliable, and flexible path planning in complex environments.