HapFIC: An Adaptive Force/Position Controller for Safe Environment Interaction in Articulated Systems.

Haptic interaction is essential for the dynamic dexterity of animals, which seamlessly switch from an impedance to an admittance behaviour using the force feedback from their proprioception. However, this ability is extremely challenging to reproduce in robots, especially when dealing with complex interaction dynamics, distributed contacts, and contact switching. Current model-based controllers require accurate interaction modelling to account for contacts and stabilise the interaction. In this manuscript, we propose an adaptive force/position controller that exploits the fractal impedance controller's passivity and non-linearity to execute a finite search algorithm using the force feedback signal from the sensor at the end-effector. The method is computationally inexpensive, opening the possibility to deal with distributed contacts in the future. We evaluated the architecture in physics simulation and showed that the controller can robustly control the interaction with objects of different dynamics without violating the maximum allowable target forces or causing numerical instability even for very rigid objects. The proposed controller can also autonomously deal with contact switching and may find application in multiple fields such as legged locomotion, rehabilitation and assistive robotics.

An Optimization-Based Locomotion Controller for Quadruped Robots Leveraging Cartesian Impedance Control.

Quadruped robots require compliance to handle unexpected external forces, such as impulsive contact forces from rough terrain, or from physical human-robot interaction. This paper presents a locomotion controller using Cartesian impedance control to coordinate tracking performance and desired compliance, along with Quadratic Programming (QP) to satisfy friction cone constraints, unilateral constraints, and torque limits. First, we resort to projected inverse-dynamics to derive an analytical control law of Cartesian impedance control for constrained and underactuated systems (typically a quadruped robot). Second, we formulate a QP to compute the optimal torques that are as close as possible to the desired values resulting from Cartesian impedance control while satisfying all of the physical constraints. When the desired motion torques lead to violation of physical constraints, the QP will result in a trade-off solution that sacrifices motion performance to ensure physical constraints. The proposed algorithm gives us more insight into the system that benefits from an analytical derivation and more efficient computation compared to hierarchical QP (HQP) controllers that typically require a solution of three QPs or more. Experiments applied on the ANYmal robot with various challenging terrains show the efficiency and performance of our controller.