Incremental sampling-based algorithm for minimum-violation motion planning
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Rus_Incremental sampling.pdf
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Author(s)
Reyes Castro, Luis I.
Chaudhari, Pratik Anil
Tumova, Jana
Karaman, Sertac
Frazzoli, Emilio
Rus, Daniela L.
Date Issued
December 2013
Journal
Proceedings of the 52nd IEEE Conference on Decision and Control
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Reyes Castro, Luis I., Pratik Chaudhari, Jana Tumova, Sertac Karaman, Emilio Frazzoli, and Daniela Rus. “Incremental Sampling-Based Algorithm for Minimum-Violation Motion Planning.” 52nd IEEE Conference on Decision and Control (December 2013).
Version
Author's final manuscript
Abstract
This paper studies the problem of control strategy synthesis for dynamical systems with differential constraints to fulfill a given reachability goal while satisfying a set of safety rules. Particular attention is devoted to goals that become feasible only if a subset of the safety rules are violated. The proposed algorithm computes a control law, that minimizes the level of unsafety while the desired goal is guaranteed to be reached. This problem is motivated by an autonomous car navigating an urban environment while following rules of the road such as “always travel in right lane” and “do not change lanes frequently”. Ideas behind sampling based motion-planning algorithms, such as Probabilistic Road Maps (PRMs) and Rapidly-exploring Random Trees (RRTs), are employed to incrementally construct a finite concretization of the dynamics as a durational Kripke structure. In conjunction with this, a weighted finite automaton that captures the safety rules is used in order to find an optimal trajectory that minimizes the violation of safety rules. We prove that the proposed algorithm guarantees asymptotic optimality, i.e., almost-sure convergence to optimal solutions. We present results of simulation experiments and an implementation on an autonomous urban mobility-on-demand system.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. School of Engineering
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/CDC.2013.6760374
