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 proposed not only to keep robot body from colliding with objects but also preserve 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.