用数字孪生+人体运动预测，实现机器人提前避障。
Digital-Twin Evaluation for Proactive Human-Robot Collision Avoidance via Prediction-Guided A-RRT*
- 基于关节级运动预测与数字孪生验证安全规划
- 50次试验中100%成功避障，余量超250mm，重规划<2秒
- 适合需要高精度人机协作的工业场景
人机协作需对长时间跨度内的人体运动进行精确预测，以实现主动避障。不同于仅依赖运动学模型的现有规划器，本文提出一种基于预测的安全规划框架，利用基于物理的数字孪生验证粒度精细的关节级人体运动预测。基于胶囊的势场将预测结果转化为碰撞风险指标，在阈值超过时触发自适应RRT*（A-RRT*）规划器。通过深度相机提取3D骨骼姿态，并使用卷积神经网络-双向长短期记忆（CNN-BiLSTM）模型预测各关节轨迹。数字孪生模型将实时人体姿态预测置于仿真机器人前方，评估动作与物理接触。该方法可预先验证规划轨迹，弥补实时更新中的延迟间隙。在50次测试中，本方法实现了100%主动避障，保持>250 mm安全距离，重规划时间低于2秒，相比仅依赖运动学的规划器展现出更优的精度与可靠性。
原文摘要 · Abstract (English)
Human-robot collaboration requires precise prediction of human motion over extended horizons to enable proactive collision avoidance. Unlike existing planners that rely solely on kinodynamic models, we present a prediction-driven safe planning framework that leverages granular, joint-by-joint human motion forecasting validated in a physics-based digital twin. A capsule-based artificial potential field (APF) converts these granular predictions into collision risk metrics, triggering an Adaptive RRT* (A-RRT*) planner when thresholds are exceeded. The depth camera is used to extract 3D skeletal poses and a convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) model to predict individual joint trajectories ahead of time. A digital twin model integrates real-time human posture prediction placed in front of a simulated robot to evaluate motions and physical contacts. The proposed method enables validation of planned trajectories ahead of time and bridging potential latency gaps in updating planned trajectories in real-time. In 50 trials, our method achieved 100% proactive avoidance with > 250 mm clearance and sub-2 s replanning, demonstrating superior precision and reliability compared to existing kinematic-only planners through the integration of predictive human modeling with digital twin validation.
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