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

TL;DR

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.

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.

Figures (7)

• Figure 1: Human-robot collaboration in a pick and place task.
• Figure 2: Four avoidance spring profiles. Profile 1 (gray): $f_{max}=-40, \, \sigma=0.09$; Profile 2 (light blue): $f_{max}=-60, \, \sigma=0.09$; Profile 3 (medium blue): $f_{max}=-40, \, \sigma=0.18$; Profile 4 (dark blue): $f_{max}=-60, \, \sigma=0.18$.
• Figure 3: Multi-robot simulation. Robot 1 and 2 are for two-robot experiment. Robot 1-3 are for three-robot experiment.
• Figure 4: Robot 1 avoiding Robot 2 (left) and a human (right) with the same VMC avoidance spring. In both cases, Robot 1 retreats as the other agent approaches, then returns.
• Figure 5: Two UR5 robots transport cubic blocks (black squares) toward their target cells (dashed arrows). The intersection of trajectories represents the potential conflict region.