Online Collision Avoidance for Human-Robot Collaborative Interaction
Concerning Safety and Efficient
With the development of robot technology and the arrival of industry 4.0
era, society pays more attention to col- laboration and interaction
between human and robots. However, safety is still main concern in the
development of human-robot collaboration. In this paper, a novel
real-time collision avoidance approach for mobile manipulator is
proposed by considering the motion status of the human, which includes
the relative minimum distance and velocity (both magnitude and
direction) between the robot and the human. The distance and velocity of
the human hand are first estimated online using a vision sensor, and
then defined as danger factors in the potential function of the
potential field. The novel potential function proposed in this paper
considers not only the safety problem, but also the efficient problem,
i.e., the manipulator can make smart control decision to avoid the
collision according to the relative velocity in case of the cross over.
To overcome the local minimum problem and choose a best motion
direction, we propose a motion sampling mechanism for motion planning.
For each sample, the robot calculates the potential function to evaluate
the safety and efficiency, and chooses a direction which is best for
avoidance. We finally demonstrate our idea on a real mobile manipulator
platform in a simulated co-worker environment.