Deep learning improves robot motion around humans safely
Proactive Motion Planning for Human-Robot Cooperation
Robotics
Summary
Robots need to move safely when working near people, but predicting human movements is tricky. The authors created a new way to forecast how a person will move using a smart computer model. They then used these predictions to plan the robot’s path more carefully so it can avoid running into people. This approach helps robots work more smoothly and safely alongside humans.
What this means in practice
- •For industrial robot programmers: Program robotic arms to anticipate human movements and avoid collisions during collaborative tasks on factory floors.
- •For warehouse automation teams: Design robot navigation systems that proactively adjust routes to keep workers safe during shared operations.
Authors
Elena Basei, Edoardo Lamon, Matteo Saveriano, Daniele Fontanelli, Luigi Palopoli
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.