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. Note to Practitioners —The motivation of this paper is focusing on obstacle avoidance of omnidirectional mobile robots in human-robot co-transporting, application scenarios concentrated in factories and logistics warehouses, such as the mobile robot collaborates with a human (a robot teleoperated by human) transporting a table. The existing obstacle avoidance algorithm can be regarded as a local trajectory planning problem, which cannot directly adapt to co-transporting tasks in real-time. Considering the specific task if the robot cannot cooperate with human in real-time, the carried object will fall. In this paper, a local trajectory planning method is proposed, regarding the obstacle generating virtual repulsive force, regarding the human action on the robot as the virtual gravitational force, and the mobile robot under two virtual forces not only follows human action in co-transporting, but also avoids obstacles. The method shows quick calculation and good real-time performance. The method proposed is evaluated by co-transporting in Gazebo and experiments based on omnidirectional mobile robots, and ensured that the object being carried does not fall and robot can avoid collisions in the unknown environments without positioning systems.