Sensorless Robotic External Force Estimation in Uncertain Interactive Environments: A Hybrid Adaptive-Robust Kalman Filtering Approach

Accurate robotic external force estimation is fundamental to sensorless physical human-robot interaction (pHRI), as it enables robots to interact with environments compliantly and safely. While the Kalman filter-based generalized momentum force estimation method (KF-GM) is widely adopted, its static covariance matrices and Gaussian noise assumption constrain adaptability and robustness, degrading estimation accuracy. This paper proposes a novel Hybrid Adaptive-Robust Kalman Filtering Approach (HARKF) integrating adaptive Kalman filter (AKF) and robust Kalman filter (RKF), with real-time covariance adjustment and outlier rejection, substantially improving adaptability and robustness. However, the fusing of AKF and RKF introduces inherent inter-filter coupling interferences, significantly compromising estimation accuracy due to incompatible noise adaptation mechanisms. Therefore, a noise-type-based module decoupling scheme and a parameter transfer mechanism are proposed, establishing synergistic collaboration between AKF and RKF, where their complementary mechanisms enable reciprocal reinforcement. The decoupling scheme eliminates cross-coupling through noise characteristic analysis, thus preserving system adaptability while enhancing disturbance robustness, resulting in significantly enhanced force estimation accuracy. The transfer mechanism resolves inter-filter parameter conflicts, thereby considerably improving filtering continuity and estimation robustness. Experimental results indicate that compared with existing Kalman filter-based methods, HARKF exhibits superior force estimation accuracy across diverse interactive scenarios.