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

2025-12-16

AI Summary

Addressing the challenge of simultaneously ensuring collision avoidance, real-time responsiveness, and ergonomic safety in industrial human–robot collaboration, this paper proposes the Configuration-Space Ergonomic Field (CSEF)—the first continuously differentiable ergonomic quality field enabling gradient-driven real-time optimization. Innovatively integrating joint-weighted metrics and task-conditioned modeling, CSEF directly optimizes ergonomic criteria in configuration space, overcoming accuracy and efficiency limitations of conventional task-space approaches. The contributions include: (i) a CSEF modeling algorithm, (ii) a joint-space gradient-based planner, and (iii) an impedance-control-compatible framework. Evaluated on dual-arm robotic collaborative drilling and co-holding tasks, the method reduces ergonomic scores by 10.31% and 5.60%, respectively, significantly decreases activation of critical muscle groups, and achieves superior trajectory tracking accuracy and faster convergence of ergonomic cost compared to point-to-point baselines.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Develops ergonomic motion planning for human-robot collaboration
• Creates a field to quantify and optimize human ergonomics in real-time
• Reduces ergonomic risk and muscle fatigue in industrial tasks

Innovation

• Human-Centric Configuration Space Ergonomic Field for ergonomic planning
• Gradient-based motion planning using differentiable ergonomic field gradients
• Real-time algorithm integrating ergonomic metrics with joint weighting