为机器人协作设计可实时计算的人体工学场，提升安全与效率
Interactive Motion Planning for Human-Robot Collaboration Based on Human-Centric Configuration Space Ergonomic Field
- 构建连续可微的体感工学场（CSEF），融合关节权重与任务条件
- 实测在钻孔与双手搬运中降低10.31%与5.60%工学评分
- 适合需实时响应的协作机器人系统，尤其关注人体健康
工业人机协作需要无碰撞、快速响应且人体工学安全的运动规划以降低疲劳与肌肉骨骼风险。本文提出配置空间工学场（CSEF），一种定义在人体关节空间上的连续可微场，用于量化工学质量并提供梯度以支持实时规划。通过整合已有指标与关节加权及任务条件，高效构建CSEF，并集成至基于梯度的规划器，兼容阻抗控制机器人。在2自由度基准测试中，基于CSEF的规划成功率更高，工学成本更低，计算更快。硬件实验使用双臂机器人在单手引导、协作钻孔和双手共运任务中验证，相较点对点基线，显著降低工学成本，更贴近优化关节目标，减少关键肌群激活。在协作钻孔任务中平均工学评分下降10.31%，双手共运任务下降5.60%，证实其在实际部署中的有效性。
原文摘要 · Abstract (English)
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.
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