Robot motion planning for sensor-based control with uncertainties

This paper describes a new representation for sensing and control uncertainties in sensor-based robot control, and presents motion planning algorithms that use this representation. The planning algorithms employ a robot's sensing capabilities as needed to accomplish a task, by activating sensors at points or intervals during motion. A sensor is represented by three quantities: a domain, which is the set of robot configurations at which a valid measurement can be taken; an absolute sensing uncertainty held, which describes the sensor's absolute (global) accuracy; and an incremental motion uncertainty, which describes the sensor's relative (pertaining to displacements) accuracy. Control uncertainty represents the ability of a controller to drive the measured error near zero. These descriptions of sensing and control capability determine the evolution of uncertainty in a sensor-based motion plan. Two complementary algorithms for motion planning with the new representation are presented. One is a backprojection algorithm which searches globally (in configuration space and the available sensors) for ways to achieve a particular subgoal. The other searches locally (in path space) to satisfy all the constraints in a planning problem. Examples of uncertainty evaluation and motion planning in two degrees of freedom are presented.

[1]  Stephen J. Buckley.  Planning Compliant Motion Strategies , 1989, Int. J. Robotics Res..

[2]  Russell H. Taylor,et al.  Automatic Synthesis of Fine-Motion Strategies for Robots , 1984 .

[3]  Peter K. Allen,et al.  Automated tracking and grasping of a moving object with a robotic hand-eye system , 1993, IEEE Trans. Robotics Autom..

[4]  John T. Wen,et al.  A global approach to path planning for redundant manipulators , 1993, [1993] Proceedings IEEE International Conference on Robotics and Automation.

[5]  Armando Fox,et al.  Exploiting visual constraints in the synthesis of uncertainty-tolerant motion plans. II. The nondirectional backprojection , 1993, [1993] Proceedings IEEE International Conference on Robotics and Automation.

[6]  Arindam Ghosh,et al.  Optimum path planning for robot manipulators amid static and dynamic obstacles , 1993, IEEE Trans. Syst. Man Cybern..

[7]  Armando Fox,et al.  Exploiting visual constraints in the synthesis of uncertainty-tolerant motion plans , 1995, IEEE Trans. Robotics Autom..

[8]  Seth Hutchinson,et al.  Task Planner for Simultaneous Fulfillment of Operational, Geometric and Uncertainty-Reduction Goals , 1988 .

[9]  D. E. Whitney,et al.  Historical Perspective and State of the Art in Robot Force Control , 1987 .

[10]  Avinash C. Kak,et al.  Spar: A Planner that Satisfies Operational and Geometric Goals in Uncertain Environments , 1990, AI Mag..

[11]  Arthur C. Sanderson,et al.  Sensing and control uncertainty fields in sensor-based motion planning , 1994, Other Conferences.

[12]  Leo Dorst,et al.  The geometrical representation of path planning problems , 1991, Robotics Auton. Syst..

[13]  Rodney A. Brooks,et al.  Symbolic Error Analysis and Robot Planning , 1982 .

[14]  Jean-Claude Latombe,et al.  Planning the Motions of a Mobile Robot in a Sensory Uncertainty Field , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Jérôme Barraquand,et al.  Path planning through variational dynamic programming , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[16]  Dinesh K. Pai,et al.  Uncertainty and Compliance of Robot Manipulators with Applications to Task Feasibility , 1991, Int. J. Robotics Res..

[17]  Jean-Claude Latombe,et al.  Robot Motion Planning with Uncertainty in Control and Sensing , 1991, Artif. Intell..