Fault Joint Detection and Adaptive Fault-Tolerant Control of Legged Robots Under Joint Partial Failures

Legged robots employ multiple joint actuators that are susceptible to abrupt partial failures during prolonged operation. Joint failures take multiple forms. They are not limited to complete lockout failures, which are the main focus of existing literature. They also include partial torque tracking failures, where actuators only partially respond to control commands. This letter investigates fault joints detection and fault rates estimation for legged robots with partial joint failures under model predictive control (MPC) and whole-body control frameworks. To handle these problems, a control framework named GRUFD-FTC is proposed. Firstly, we have created a dataset for Unitree A1 robots under different numbers of fault joints and different torque retention rates, and have open-sourced it. In addition, a gait recurrent unit based fault detector is proposed to simultaneously detect multiple joints partial failure during robot movement and then output initial joint torque retention rates. Subsequently, a fault-tolerant controller based on joint states is proposed to accurately estimate the joint torque retention rates. Finally, simulations and real-world experiments have verified the effectiveness of the proposed algorithms.