Sensorless robot collision detection based on fuzzy momentum observer

During physical human-robot interaction, unwanted collisions occur, which also threaten human safety. That is why different collision detection algorithms are proposed for safe collaboration between human and robots. The current detection methods are model-based methods without using additional sensors. However, the estimation of the collision force using a model-based method still has the problem of an unavoidable trade-off between the collision sensitivity and the reduction of the peaking value, and, in addition, the immunity to the model uncertainties. In this paper, an improved momentum observer using fuzzy system is proposed for robot collision detection. First, to avoid using the robot's inverse inertia matrix, the generalized moment state equations based on the robot's dynamics are deduced. Second, the extended linear momentum observer is built based on robot generalized momentum equations. Third, a fuzzy system is designed to select an appropriate bandwidth for the observer. In the design of this fuzzy system, the estimation error is considered as a fuzzy input, and the observer's bandwidth is considered as its output. This fuzzy generalized momentum observer (FGMO) is compared with the existing generalized moment observers (GMOs) and has been proven its ability to overcome the mentioned above issues due to its ability to intelligently adjust the online filter parameters during the observation process. Finally, simulations are conducted with a 6-degree-of-freedom (6-DOF) industrial robot manipulator, and the obtained results illustrate the effectiveness of proposed approach in term of sensitivity and peaking value.