Sensorless environment stiffness and interaction force estimation for impedance control tuning in robotized interaction tasks

Abstract

Industrial robots are increasingly used to perform tasks requiring an interaction with the surrounding environment (e.g., assembly tasks). Such environments are usually (partially) unknown to the robot, requiring the implemented controllers to suitably react to the established interaction. Standard controllers require force/torque measurements to close the loop. However, most of the industrial manipulators do not have embedded force/torque sensor(s) and such integration results in additional costs and implementation effort. To extend the use of compliant controllers to sensorless interaction control, a model-based methodology is presented in this paper. Relying on sensorless Cartesian impedance control, two Extended Kalman Filters (EKF) are proposed: an EKF for interaction force estimation and an EKF for environment stiffness estimation. Exploiting such estimations, a control architecture is proposed to implement a sensorless force loop (exploiting the provided estimated force) with adaptive Cartesian impedance control and coupling dynamics compensation (exploiting the provided estimated environment stiffness). The described approach has been validated in both simulations and experiments. A Franka EMIKA panda robot has been used. A probing task involving different materials (i.e., with different - unknown - stiffness properties) has been considered to show the capabilities of the developed EKFs (able to converge with limited errors) and control tuning (preserving stability). Additionally, a polishing-like task and an assembly task have been implemented to show the achieved performance of the proposed methodology.

Design of Dynamics Invariant LSTM for Touch Based Human–UAV Interaction Detection

“…If good knowledge of the robotic arm is available, then the external force acting on the object can be estimated. Such a method was adopted in [17], where an extended Kalman filter is used to estimate the external forces acting on the robotic arm and implement a force controller for various tasks. Note that in this method, the robot parameters are considered to be known prior to the experiment.…”

“…The adopted neural network structure consists of two LSTM layers with 200 and 100 units respectively. The LSTM layers implicitly estimate the force exerted by the human, as mentioned in Section III, by modelling (15,16,17). It is followed by a dense layer whose neurons are activated using the rectified linear unit (ReLU).…”

Sensorless Optimal Switching Impact/Force Controller

“…[33] made use of a notch-type friction-free disturbance observer for sensorless force control purposes. [34] proposed a sensorless control framework to implement a high-performance force-tracking controller. The estimation of the interaction environment stiffness, together with the estimation of the interaction force, have been used to design the control framework.…”

“…It has to be highlighted that an observer for the estimation of the external interaction is proposed due to the fact that compliance control is employed. Therefore, this observer can be also adopted in order to close the control loop [44], [45], [34].…”

Elasto-Geometrical Model-Based Control of Industrial Manipulators Using Force Feedback: Application to Incremental Sheet Forming

“…Current research in manufacturing processes for Industry 4.0 is enhancing Computer-Aided Manufacturing (CAM) techniques to lower the environmental, human time and investment cost of producing a good-quality part [1]. For this purpose, the research field is transitioning from relying exclusively on traditional CNC machines toward using much more versatile and affordable industrial manipulators.…”