Incremental Sampling-Based Algorithm for Minimum-Violation
Motion Planning

D 2013

Incremental Sampling-Based Algorithm for Minimum-Violation Motion Planning

REYES CASTRO, Luis Ignacio, Pratik CHAUDHARI, Jana TŮMOVÁ, Sertac KARAMAN, Emilio FRAZZOLI et. al.

Basic information

Original name

Incremental Sampling-Based Algorithm for Minimum-Violation Motion Planning

Authors

REYES CASTRO, Luis Ignacio (840 United States of America), Pratik CHAUDHARI (840 United States of America), Jana TŮMOVÁ (203 Czech Republic, guarantor, belonging to the institution), Sertac KARAMAN (840 United States of America), Emilio FRAZZOLI (840 United States of America) and Daniela RUS (840 United States of America)

Edition

Florence, Italy, Proceedings of the IEEE 52nd Annual Conference on Decision and Control (CDC), 2013, p. 3217-3224, 8 pp. 2013

Publisher

IEEE

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

storage medium (CD, DVD, flash disk)

RIV identification code

RIV/00216224:14330/13:00081960

Organization unit

Faculty of Informatics

ISBN

978-1-4673-5717-3

ISSN

DOI

http://dx.doi.org/10.1109/CDC.2013.6760374

UT WoS

000352223503105

Keywords in English

motion planning; temporal logic; sampling-based planning; formal methods

Abstract

V originále

This paper studies the problem of control strategy synthesis for dynamical systems with differential constraints to fulfill a given reachability goal specification 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.

Links

LH11065, research and development project

Name: Řízení a ověřování vlastností komplexních hybridních systémů (Acronym: Řízení a ověřování vlastností komplexních hybridní)

Investor: Ministry of Education, Youth and Sports of the CR

MUNI/A/0760/2012, interní kód MU

Name: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace II. (Acronym: FI MAV II.)

Investor: Masaryk University, Category A

Citovat

REYES CASTRO, Luis Ignacio, Pratik CHAUDHARI, Jana TŮMOVÁ, Sertac KARAMAN, Emilio FRAZZOLI and Daniela RUS. Incremental Sampling-Based Algorithm for Minimum-Violation Motion Planning. In Proceedings of the IEEE 52nd Annual Conference on Decision and Control (CDC), 2013. Florence, Italy: IEEE, 2013, p. 3217-3224. ISBN 978-1-4673-5717-3. Available from: https://dx.doi.org/10.1109/CDC.2013.6760374.

@inproceedings{1163972,
   author = {Reyes Castro, Luis Ignacio and Chaudhari, Pratik and Tůmová, Jana and Karaman, Sertac and Frazzoli, Emilio and Rus, Daniela},
   address = {Florence, Italy},
   booktitle = {Proceedings of the IEEE 52nd Annual Conference on Decision and Control (CDC), 2013},
   doi = {http://dx.doi.org/10.1109/CDC.2013.6760374},
   keywords = {motion planning; temporal logic; sampling-based planning; formal methods},
   howpublished = {paměťový nosič},
   language = {eng},
   location = {Florence, Italy},
   isbn = {978-1-4673-5717-3},
   pages = {3217-3224},
   publisher = {IEEE},
   title = {Incremental Sampling-Based Algorithm for Minimum-Violation Motion Planning},
   year = {2013}
}

TY  - JOUR
ID  - 1163972
AU  - Reyes Castro, Luis Ignacio - Chaudhari, Pratik - Tůmová, Jana - Karaman, Sertac - Frazzoli, Emilio - Rus, Daniela
PY  - 2013
TI  - Incremental Sampling-Based Algorithm for Minimum-Violation Motion Planning
PB  - IEEE
CY  - Florence, Italy
SN  - 9781467357173
KW  - motion planning
KW  - temporal logic
KW  - sampling-based planning
KW  - formal methods
N2  - This paper studies the problem of control strategy synthesis for dynamical systems with differential constraints to fulfill a given reachability goal specification 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.
ER  -

REYES CASTRO, Luis Ignacio, Pratik CHAUDHARI, Jana TŮMOVÁ, Sertac KARAMAN, Emilio FRAZZOLI and Daniela RUS. Incremental Sampling-Based Algorithm for Minimum-Violation Motion Planning. In \textit{Proceedings of the IEEE 52nd Annual Conference on Decision and Control (CDC), 2013}. Florence, Italy: IEEE, 2013, p.~3217-3224. ISBN~978-1-4673-5717-3. Available from: https://dx.doi.org/10.1109/CDC.2013.6760374.

Detailed Information on Publication Record