A Sensorless Force Estimation Algorithm for Robot Based on Model Compensation

Acquisition of external torque information is an important challenge for robot interaction with the environment. To avoid the high-cost problem associated with installing force sensors, a novel model-based sensorless interaction force estimation algorithm is proposed for accurate and fast estimation of interaction force and torque. To enhance the model's accuracy, the first step involves acquiring dynamics parameters through model identification, followed by conducting nonlinear friction compensation. Subsequently, an algorithm combining extended state observer (ESO) and generalized momentum is introduced to evaluate the system's interaction force and torque, which improves the rapidity and accuracy of the estimation. Ultimately, the proposed approach's efficacy was validated by conducting experiments on a six-degree-of-freedom robot as the primary testing platform.