Sensorless Optimal Interaction Control Exploiting Environment Stiffness Estimation

Industrial robots are increasingly used to perform tasks that require an interaction with the surrounding environment (e.g., assembly tasks). Such environments are usually (partially) unknown to the robot (in terms of dynamic characteristics), demanding the implemented controllers to suitably react to the established interaction. Standard controllers require force/torque measurements to close the loop, making it, if possible, to adapt the robot behavior to the specific environment. However, most of the industrial manipulators do not have embedded force/torque sensor(s), which entails additional effort in terms of costs and implementation for their integration in the robotic setup. To extend the use of sensorless compliant controllers to force control, a robot–environment interaction dynamics model-based methodology is presented in this article. Relying on the sensorless Cartesian impedance control, an extended Kalman filter (EKF) is proposed to estimate the stiffness of an interaction environment. Exploiting the provided estimation, the robot–environment coupled dynamic modeling is used to design an optimal LQR interaction controller to close the force loop. The control gains can be analytically computed by solving the related Riccati equation, as such gains are a function of the impedance control and environment parameters. In addition, the interaction force can be estimated to close the force loop, making the sensorless robot able to perform the target interaction task. The described approach has been validated with experiments by analyzing two scenarios: a probing task and a closing of a plastic (i.e., compliant) box with a snap-fit closure mechanism. The performance of the proposed control framework has been evaluated, highlighting the capabilities of the EKF and the optimal LQR interaction controller. Finally, the proposed control schema is enhanced by the adaptation of the EKF for the estimation of the external wrench. Two additional experiments are provided to show the improvements on the control schema (a polishing-like task and an assembly task). The Franka EMIKA panda robot has been used as the reference robotic platform for experimental validation.