Digital-Twin Evaluation for Proactive Human-Robot Collision Avoidance via Prediction-Guided A-RRT*
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866912626905186304 |
|---|---|
| author | Murugesan, Vadivelan Mathiazhagan, Rajasundaram Joshi, Sanjana Arab, Aliasghar |
| author_facet | Murugesan, Vadivelan Mathiazhagan, Rajasundaram Joshi, Sanjana Arab, Aliasghar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_03496 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Digital-Twin Evaluation for Proactive Human-Robot Collision Avoidance via Prediction-Guided A-RRT* Murugesan, Vadivelan Mathiazhagan, Rajasundaram Joshi, Sanjana Arab, Aliasghar Robotics 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. |
| title | Digital-Twin Evaluation for Proactive Human-Robot Collision Avoidance via Prediction-Guided A-RRT* |
| topic | Robotics |
| url | https://arxiv.org/abs/2510.03496 |