Distributed Virtual Model Control for Scalable Human-Robot Collaboration in Shared Workspace

TL;DR

This paper introduces a decentralized, virtual model control framework for scalable, safe human-robot collaboration that effectively prevents deadlocks and adapts to different team sizes without structural changes.

Contribution

The proposed approach is the first to integrate virtual model control with decentralized, force-based deadlock detection and negotiation for scalable human-robot collaboration.

Findings

Reduces deadlock probability from 61.2% to zero in experiments.

Successfully scales to four robots and two humans in simulation.

Maintains safe inter-agent separation of around 20 cm.

Abstract

We present a decentralized, agent agnostic, and safety-aware control framework for human-robot collaboration based on Virtual Model Control (VMC). In our approach, both humans and robots are embedded in the same virtual-component-shaped workspace, where motion is the result of the interaction with virtual springs and dampers rather than explicit trajectory planning. A decentralized, force-based stall detector identifies deadlocks, which are resolved through negotiation. This reduces the probability of robots getting stuck in the block placement task from up to 61.2% to zero in our experiments. The framework scales without structural changes thanks to the distributed implementation: in experiments we demonstrate safe collaboration with up to two robots and two humans, and in simulation up to four robots, maintaining inter-agent separation at around 20 cm. Results show that the method shapes robot behavior intuitively by adjusting control parameters and achieves deadlock-free operation across team sizes in all tested scenarios.