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This Time the Robot Settles for a Cost: A Quantitative Approach to Temporal Logic Planning with Partial Satisfaction (2015)
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BibTeX
@MISC{Lahijanian15thistime,
author = {Morteza Lahijanian and Shaull Almagor and Dror Fried and Lydia E. Kavraki and Moshe Y. Vardi},
title = {This Time the Robot Settles for a Cost: A Quantitative Approach to Temporal Logic Planning with Partial Satisfaction},
year = {2015}
}
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Abstract
The specification of complex motion goals through temporal logics is increasingly favored in robotics to narrow the gap between task and motion planning. A major limiting factor of such logics, however, is their Boolean satisfaction condition. To relax this limitation, we introduce a method for quantifying the satisfaction of co-safe linear temporal logic specifications, and propose a planner that uses this method to synthesize robot trajectories with the optimal satisfaction value. The method assigns costs to violations of specifications from user-defined proposition costs. These violation costs define a distance to satisfaction and can be computed algorithmically using a weighted automaton. The planner utilizes this automaton and an abstraction of the robotic system to construct a product graph that captures all possible robot trajectories and their distances to satisfaction. Then, a plan with the minimum distance to satisfaction is generated by employing this graph as the high-level planner in a synergistic planning framework. The efficacy of the method is illustrated on a robot with unsatisfiable specifications in an office environment.
Keyphrases
temporal logic planning quantitative approach robot settle partial satisfaction boolean satisfaction condition temporal logic method assigns cost possible robot trajectory un-satisfiable specification optimal satisfaction value weighted automaton robotic system robot trajectory motion planning minimum distance office environment user-defined proposition cost high-level planner product graph violation cost co-safe linear temporal logic specifica-tions synergistic planning framework complex motion goal
