Improved impedance/admittance switching controller for the interaction with a variable stiffness environment

Abstract

Hybrid impedance/admittance control aims to provide an adaptive behavior to the manipulator in order to interact with the surrounding environment. In fact, impedance control is suitable for stiff environments, while admittance control is suitable for soft environments/free motion. Hybrid impedance/admittance control, indeed, allows modulating the control actions to exploit the combination of such behaviors. While some work has addressed the proposed topic, there are still some open issues to be solved. In particular, the proposed contribution aims: (i) to satisfy the continuity of the interaction force in the switching from impedance to admittance control when a feedforward velocity term is present; and (ii) to adapt the switching parameters to improve the performance of the hybrid control framework to better exploit the properties of both impedance and admittance controllers. The proposed approach was compared in simulation with the standard hybrid impedance/admittance control in order to show the improved performance. A Franka EMIKA panda robot was used as a reference robotic platform to provide a realistic simulation.

Optimized Residual Action for Interaction Control with Learned Environments

“…The expression of Cartesian impedance control with robot dynamics compensation can be written as [22]:…”

How to deal with the complexity in robotic systems?

“…Emerging technologies for robot motion control deal with the complexity of robot tasks, safety, and environments. Within the context of offshore robotics, the paper entitled Reinforcement learning-based control for offshore crane load-landing operations [6] studies the motion control of the offshore crane system, such as the load-landing or -lifting operations in interconnection with a vessel under environmental conditions. During the crane motion, the impact between the loads and the vessels is to be minimized and controlled to avoid serious injuries and extensive damage.…”

“…Therefore, machine learning algorithms could play a crucial role in multiple offshore crane operations, including load-landing operations. Specifically, the authors [6] attempted to utilize reinforcement learning (RL) algorithms for crane motion control. More specifically, they used the Q-learning algorithm to develop optimal control sequences for the offshore crane's actuators to minimize the impact velocity between the crane's load and the moving vessel.…”

Hybrid impedance and admittance control for optimal robot–environment interaction

“…The first problem is that existing impedance adaptation methods [24][25][26] can only obtain insufficient first-order impedance models in the second-order unknown environment. The second problem is that existing hybrid control methods [28][29][30][31][32] do not consider the interaction with the second-order environment. Therefore, this inspires us to propose a unified scheme to optimally obtain and implement the target impedance model for the manipulator subjected to the second-order unknown environment.…”

“…As an improvement of ref. [29], a hybrid controller that can adapt the switching period and duty cycle was developed to improve the control performance [30]. In ref.…”