Real-Time Trajectory Planning and Obstacle Avoidance for Human-Robot Co-Transporting

In this paper, a real-time obstacle avoidance approach and a trajectory planning method are proposed to avoid collisions to move an object jointly by a human and an omnidirectional mobile robot. Different from many existing approaches of local trajectory planning, the proposed method called Multiscale Local Perception Region Approach (MLPRA) is specially designed for obstacle avoidance of omnidirectional wheeled robots with direction constraints of human guidance, which can respond fast to dynamic obstacles and guarantee the safety of the robot. To solve the problem that the position of the human hand is difficult to obtain by visual sensing due to visual occlusion, a simple mechanism that can indirectly measure the change of the position of the human hand is designed, and further a method to follow the human's intention based on this mechanism is proposed. Finally, the simulation environment on the Gazebo simulation platform is built to verify the feasibility and effectiveness of our proposed methods. Experimental results show that after embedding proposed methods into the omnidirectional mobile robot, obstacles can be effectively avoided in co-transporting processes.