Fault-tolerant navigation framework for omnidirectional mobile robots under actuator failures via an enhanced potential field

Omnidirectional mobile robots are used widely in logistics and manufacturing because of their high maneuverability in confined spaces. However, actuator failures and dynamic obstacles pose serious challenges, often degrading mobility and compromising safety. Earlier studies have addressed fault-tolerant control and obstacle avoidance separately, but few have considered integrated solutions that maintain reliable navigation under actuator degradation while ensuring timely avoidance of moving obstacles. This article presents a fault-tolerant navigation framework for a three-wheeled omnidirectional robot that integrates fault diagnosis, model switching, an enhanced potential field, and nonlinear model predictive control. Actuator faults are detected using an interacting multiple model approach, facilitating a seamless transition from a three-wheel holonomic model to a two-wheel nonholonomic model. To address reduced mobility after a fault, an enhanced potential field method is developed by incorporating predicted obstacle positions and directionally weighted repulsive forces, which generate smoother and earlier avoidance maneuvers. The resultant trajectories are tracked by nonlinear model predictive control to ensure accurate and constraint-aware path-following. Simulation results demonstrate that the proposed framework avoids both static and dynamic obstacles, maintains trajectory stability under an actuator failure, and greatly reduces abrupt maneuvers compared to conventional potential field approaches. These results highlight the potential of the proposed framework to advance robust and reliable navigation for omnidirectional mobile robots operating in dynamic and uncertain industrial environments.