Adaptive Gain Control for a Steady Human-Robot Cooperation

In this paper, a force control is adopted to help an operator manipulate a robotic arm directly and efficiently in a path planning task. To achieve this, we mount a 6-DOF force sensor on the tip of the robotic arm. The operator can manipulate the robotic arm easily by pushing and pulling the force sensor. However, the main problem faced in this work is when the robot is pulled to a target by the operator, its end-effector will contact with surroundings with the result that the contact force repels the end-effector from the target. Thus, the robot attempts to attain the target position since there is an error between its current and target positions. Previous studies have shown that conservative control can suppress the rebounding effect, but maintaining it will cause the operator to suffer excessive resistance in subsequent operations. To solve this problem, we propose a collision rate to detect any collision by analyzing force behavior. Then, we use this rate to adjust the control gain of robot behavior. The proposed method was validated on an industrial robotic arm. The result shows that the robot produced a small resistance for manual manipulation and avoided bouncing behavior when touching any surroundings.