Real-time collision avoidance in teleoperated whole-sensitive robot arm manipulators

In traditional teleoperation systems, the human operator is saddled with two distinct tasks: 1) moving the robot arm to its desired position, and 2) avoiding obstacles that can obstruct the robot arm manipulator. To accomplish these tasks, an area of current teleoperation research concentrates on providing the operator with as much input information about the task site as possible using, for example, stereo vision or contact force feedback. These methods presume that the operators are capable of planning the motion for the entire body of a robot arm operating in a cluttered environment. However, studies have shown that the operators, first, cannot address both tasks in real time, and second, are not good at generating collision-free motion in a complex environment. Recent results in sensor-based motion planning suggest that the collision avoidance task can be handled automatically, thus freeing the operator for global control. We propose to use for this purpose whole-sensitive arm manipulators whose whole bodies are covered with a sensitive skin sensor to detect nearby objects. The data from the sensitive skin is processed by motion planning algorithms, to avoid collisions for the entire robot arm body in an unknown or time-varying environment. The result is a highly efficient, safe and robust hybrid system in which integration of control by the operator and the automatic system is done transparently and in real time. The motion of the operator-controlled master arm is either executed faithfully by the slave arm, or, to avoid collisions, is used as general guidance.