Enabling impedance-based physical human–multi–robot collaboration: Experiments with four torque-controlled manipulators

Abstract

Robotics research into multi-robot systems so far has concentrated on implementing intelligent swarm behavior and contact-less human interaction. Studies of haptic or physical human-robot interaction, by contrast, have primarily focused on the assistance offered by a single robot. Consequently, our understanding of the physical interaction and the implicit communication through contact forces between a human and a team of multiple collaborative robots is limited. We here introduce the term Physical Human Multi-Robot Collaboration (PHMRC) to describe this more complex situation, which we consider highly relevant in future service robotics. The scenario discussed in this article covers multiple manipulators in close proximity and coupled through physical contacts. We represent this set of robots as fingers of an up-scaled agile robot hand. This perspective enables us to employ model-based grasping theory to deal with multi-contact situations. Our torque-control approach integrates dexterous multi-manipulator grasping skills, optimization of contact forces, compensation of object dynamics, and advanced impedance regulation into a coherent compliant control scheme. For this to achieve, we contribute fundamental theoretical improvements. Finally, experiments with up to four collaborative KUKA LWR IV+ manipulators performed both in simulation and real world validate the model-based control approach. As a side effect, we notice that our multi-manipulator control framework applies identically to multi-legged systems, and we execute it also on the quadruped ANYmal subject to non-coplanar contacts and human interaction.

Dual-Arm Box Grabbing With Impact-Aware MPC Utilizing Soft Deformable End-Effector Pads

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Collaborative Bimanual Manipulation Using Optimal Motion Adaptation and Interaction Control

“…The images show that the remote user is operating in the bimanual mode with hybrid command mode. sequences of overlapping convex sub-domains to maximizing the feasibility probability, such as Hierarchical Quadratic Programming (H-QP) [15]- [17] and Non-Linear Programming (NLP) [18].…”

Predicting Impact-Induced Joint Velocity Jumps on Kinematic-Controlled Manipulator

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