aiqbench analysis
This research advances human-robot collaboration by integrating deep learning-based motion prediction into dynamic trajectory planning for robotic manipulators. The approach enables proactive collision avoidance and safer interaction through adaptive pathfinding that anticipates human movement in real-time.
Proactive Motion Planning for Human-Robot Cooperation
arXiv:2609.32354v1 Announce Type: new Abstract: This abstract addresses the incorporation of human motion prediction into proactive and dynamic human-aware motion planning, with the goal of enabling safe collaboration between humans and robots. A deep learning, graph-based model is used to forecast human motion and is integrated into a planning framework. This framework employs a static roadmap along with a time-variant A* algorithm to modify the trajectory of a UR5e manipulator. This method greatly improves human-robot interaction and enables proactive collision avoidance by combining precise motion forecasts with adaptive trajectory planning.
Abstract:This abstract addresses the incorporation of human motion prediction into proactive and dynamic human-aware motion planning, with the goal of enabling safe collaboration between humans and robots. A deep learning, graph-based model is used to forecast human motion and is integrated into a planning framework. This framework employs a static roadmap along with a time-variant A* algorithm to modify the trajectory of a UR5e manipulator. This method greatly improves human-robot interaction and enables proactive collision avoidance by combining precise motion forecasts with adaptive trajectory planning.
Robotics (cs.RO)
arXiv:2609.32354 [cs.RO]
(or arXiv:2609.32354v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.32354 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 178 words
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