Interactive Motion Planning for Human-Robot Collaboration Based on Human-Centric Configuration Space Ergonomic Field

TL;DR

This paper introduces the Configuration Space Ergonomic Field (CSEF), a novel ergonomic metric for real-time, safe, and efficient human-robot collaboration motion planning that improves ergonomic outcomes and reduces muscle activation.

Contribution

It develops a continuous ergonomic quality field over human joint space and integrates it into a gradient-based planner for improved ergonomic safety in human-robot tasks.

Findings

CSEF-based planning outperforms task-space ergonomic planning in success rate and speed.

Hardware experiments show reduced ergonomic scores and muscle activation.

Method achieves practical ergonomic improvements in real-world collaborative tasks.

Abstract

Industrial human-robot collaboration requires motion planning that is collision-free, responsive, and ergonomically safe to reduce fatigue and musculoskeletal risk. We propose the Configuration Space Ergonomic Field (CSEF), a continuous and differentiable field over the human joint space that quantifies ergonomic quality and provides gradients for real-time ergonomics-aware planning. An efficient algorithm constructs CSEF from established metrics with joint-wise weighting and task conditioning, and we integrate it into a gradient-based planner compatible with impedance-controlled robots. In a 2-DoF benchmark, CSEF-based planning achieves higher success rates, lower ergonomic cost, and faster computation than a task-space ergonomic planner. Hardware experiments with a dual-arm robot in unimanual guidance, collaborative drilling, and bimanual cocarrying show faster ergonomic cost reduction, closer tracking to optimized joint targets, and lower muscle activation than a point-to-point baseline. CSEF-based planning method reduces average ergonomic scores by up to 10.31% for collaborative drilling tasks and 5.60% for bimanual co-carrying tasks while decreasing activation in key muscle groups, indicating practical benefits for real-world deployment.