Collision-Free Motion Planning for Human-Robot Collaborative Safety Under Cartesian Constraint | IEEE Conference Publication | IEEE Xplore

Collision-Free Motion Planning for Human-Robot Collaborative Safety Under Cartesian Constraint


Abstract:

This paper presents a real-time motion planning and control design of a robotic arm for human-robot collaborative safety. A novel collision-free motion planning method is...Show More

Abstract:

This paper presents a real-time motion planning and control design of a robotic arm for human-robot collaborative safety. A novel collision-free motion planning method is proposed not only to keep robot body from colliding with objects but also preserve the execution of robot's original task under the Cartesian constraint of the environment. Multiple KinectV2 depth cameras are utilized to model and track dynamic obstacles (e.g. Humans and objects) inside the robot workspace. Depth images are applied to generate point cloud of segmented objects in the environment. A K-nearest neighbor (KNN) searching algorithm is used to cluster and find the closest point from the obstacle to the robot. Then a Kalman filter is applied to estimate the obstacle position and velocity. For the collision avoidance in collaborative operation, attractive and repulsive potential is generated for robot end effector based on the task specification and obstacle observation. Practical experiments show that the 6-DOF robot arm can effectively avoid an obstacle in a constrained environment and complete the original task.
Date of Conference: 21-25 May 2018
Date Added to IEEE Xplore: 13 September 2018
ISBN Information:
Electronic ISSN: 2577-087X
Conference Location: Brisbane, QLD, Australia

References

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