In traditional teleoperation systems, the human operator is saddled with two distinct tasks: I) moving the robot arm to its desired position, a nd 2) avoiding obstacles that can obstruct the arm motion. The current robot teleopera tion 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 operator is capable of planning motion for the entire body of the robot in a cluttered environment. Studies show, however, 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. To this end, it is also proposed to use whole-sensitive arm manipulators whose whole bodies are covered with a sensitive skin sensor to detect nearby objects. The data from the skin is processed by motion planning algorithms, to avoid collisions for the entire a rm body in an unknown or time-varying environment. The motion of the operator-controlled master arm is either repeated faithfully by the slave arm, or, to avoid collisions, is used as general guidance. In the latter case the slave arm attempts to be as close as possible to the positions commanded by the operator, without jeopardizing its safety. The result is an efficient, safe and robust hybrid system in which integration of control by the operator a nd the automatic system is done transparently and in real time.
In this paper, results of one implementation effort toward a motion planning system for a robot arm operating in an uncertain environment are discussed. It has been known that path planning algorithms with proven convergence can be designed for some planar and three-dimensional robot arm manipulators operating among unknown obstacles of arbitrary shapes. The attractiveness of such systems lies in their ability to operate in a complex, perhaps even time-varying, unstructured environment. Implementation of these algorithms, however, presents a variety of hardware and algorithmic problems related to, first, covering the arm with arrays of sensors to form a "sensitive skin''; second, processing real-time sensor data; third, designing complementary algorithms for step-by-step motion planning based on limited local information; and finally, integrating these components, together with global planning algorithms, in a single system. We discuss various tradeoffs and solutions implemented in the sensor-based planning system for a planar arm manipulator developed in our laboratory, and present a summary of our experiments with it.
Sensor-based robot motion planning in an uncertain environment, especially for autonomous vehicles, has attracted much attention recently. Its natural extension to 3D arm manipulators calls for a capability to sense an approaching obstacle by any point of the robot body. This paper describes an on-going research project in our laboratory on the development of a sensitive skin and its control scheme for a 3D robot arm. The skin consists of hundreds of active infra-red proximity sensors that cover the whole arm body. Details of the skin design are presented, along with the computer hardware and operating logistics, and the sensor data interpretation algorithm that connects this subsystem to the global motion planning subsystem.
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