Dynamic Obstacle Avoidance Using Nonlinear Model Predictive Control and Control Barrier Function for Ballbot Systems

This research presents a tracking control system for a ballbot designed to operate in complex environments filled with both static and dynamic obstacles. The Nonlinear Model Predictive Control (NMPC) framework is formulated to predict the future positions of the ballbot and all surrounding obstacles. This predictive capability is crucial for effective navigation, as it allows the ballbot to anticipate potential collisions in the prediction horizon. The NMPC is integrated with an optimization problem that is enhanced by Control Barrier Function (CBF) constraints. These constraints ensure that the ballbot maintains a safe and consistent distance from every obstacle, thus preventing collisions. Additionally, an Extended State Observer (ESO) is implemented to observe and compensate for uncertain disturbances in the ballbot’s movements, as well as to estimate immeasurable variables that might affect its performance. Various simulation scenarios are conducted to thoroughly test and validate the effectiveness of this approach in achieving precise tracking control and reliable collision avoidance in environments with a large number of obstacles.