{"ID":2861150,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.03496","arxiv_id":"2510.03496","title":"Digital-Twin Evaluation for Proactive Human-Robot Collision Avoidance via Prediction-Guided A-RRT*","abstract":"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 \u003e 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.","short_abstract":"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 v...","url_abs":"https://arxiv.org/abs/2510.03496","url_pdf":"https://arxiv.org/pdf/2510.03496v1","authors":"[\"Vadivelan Murugesan\",\"Rajasundaram Mathiazhagan\",\"Sanjana Joshi\",\"Aliasghar Arab\"]","published":"2025-10-03T20:20:55Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}