Abstract
Over the course of the past decade, we have witnessed a huge expansion in robotic applications, most notably from well-defined industrial environments into considerably more complex environments. The obstacles that these environments often contain present robotics with a new challenge - to equip robots with a real-time capability of avoiding them. In this paper, we propose a magnetic-field-inspired navigation method that significantly has several advantages over alternative systems. Most importantly, 1) it guarantees obstacle avoidance for both convex and non-convex obstacles, 2) goal convergence is still guaranteed for point-like robots in environments with convex obstacles and non-maze concave obstacles, 3) no prior knowledge of the environment, such as the position and geometry of the obstacles, is needed, 4) it only requires temporally and spatially local environmental sensor information, and 5) it can be implemented on a wide range of robotic platforms in both 2D and 3D environments. The proposed navigation algorithm is validated in simulation scenarios as well as through experimentation. The results demonstrate that robotic platforms, ranging from planar point-like robots to robot arm structures such as the Baxter robot, can successfully navigate toward desired targets within an obstacle-laden environment.