An Obstacle Avoidance Path Planning Algorithm for Autonomous Mobile Robots

The ability of efficient path planning and obstacle avoidance is a key requirement for mobile robots in most real-world applications. This paper presents a novel Bug-based path-planning algorithm for mobile robots. Building upon the Bug-2 algorithm, the proposed Shortcut Bug algorithm generates shorter paths compared to existing deterministic methods by reducing unnecessary boundary following around obstacles. The proposed method does not require advanced capabilities such as distance estimation or obstacle edge detection and allows the robot to leave the boundary of the obstacle earlier than the existing algorithms. Comprehensive simulations across various maps and obstacles revealed that the proposed algorithm produces paths that are, on average, 7.93% shorter than its closest counterpart. Additionally, the percentage deviation from the optimal path is at least 19.4% lower than that of existing algorithms, demonstrating the proposed algorithm's ability to generate paths closer to optimal solutions.