Implementation and Validation of Obstacle Avoidance Algorithms on a Self-Balancing Robot

Driving dynamically-stable robots such as ballbots through complex environments poses significant challenges due to their unique dynamics and underactuated nature. This study presents the evaluation of a state-of-the-art sampling-based path planning algorithm, called Neural Informed Rapidly-exploring Random Tree Star (NIRRT*), on a payload-carrying ballbot, namely MiaPURE, for autonomous navigation applications. Extensive testing was conducted to assess the algorithm's effectiveness in navigating challenging indoor environments with static and dynamic obstacles, under various speeds and payload conditions. Results demonstrated overall effectiveness of the NIRRT* algorithm in avoiding collisions with static and dynamic obstacles. NIRRT* algorithm performance was also compared against Dynamic Window Approach (DWA), and two human-controlled methods (i.e., on-board control using hands-free control, remote control using joystick teleoperation). NIRRT* demonstrated similar completion times and no collisions compared to DWA, while also achieving completion times and collision rates comparable to teleoperation. Finally, we demonstrated MiaPURE’s autonomous navigation in an indoor setting, successfully navigating at 0.4 m/s about 20 m and passing through areas as narrow as 90 cm, highlighting its potential for practical deployment in indoor payload-carrying and human-riding applications.