Tessa Lau
AI Magazine
This paper describes a planning program that synthesizes compliant motion strategies, in which an object in the grasp of a robot slides along obstacles, in an attempt to reach a goal region. The input to the planner is a model of the task geometry, a start region, and a goal region. To make the planning problem tractable, we reduce the task geometry to a finite state space, whose states are collections of vertices, edges, and faces from the configuration space of the robot. Strategy synthesis is complicated by uncertainty in the start ing configuration of the robot and in robot sensing and con trol. The planner synthesizes compliant motions that are guaranteed to perform state transitions despite uncertainty. Using best first search, compliant motions are synthesized until a strategy is found from the start state to a goal state. A strategy may require that the robot stop in an intermediate state, using sensors to determine the next commanded motion. © 1989, Sage Publications. All rights reserved.
Tessa Lau
AI Magazine
Ryan Johnson, Ippokratis Pandis
CIDR 2013
David W. Jacobs, Daphna Weinshall, et al.
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fahiem Bacchus, Joseph Y. Halpern, et al.
IJCAI 1995