research-article

A viability approach for fast recursive feasible finite horizon path planning of autonomous RC cars

Authors:

Alexander Liniger,

John LygerosAuthors Info & Claims

HSCC '15: Proceedings of the 18th International Conference on Hybrid Systems: Computation and Control

Pages 1 - 10

https://doi.org/10.1145/2728606.2728620

Published: 14 April 2015 Publication History

Abstract

We consider a viability based approach to guarantee recursive feasibility of a finite horizon path planner. The path planner is formulated as a hybrid system for which a difference inclusion reformulation is derived by exploiting the special structure of the problem. Based on this approximation, the viability kernel, which characterizes all safe states and the corresponding safe controls, can be calculated. Using the set of safe controls the computation time of the on-line path planning can be reduced, by only generating viable trajectories. Finally, a condition characterizing the unsafe set in case of on-line obstacle avoidance is derived.

References

[1]

J.-P. Aubin, A. M. Bayen, and P. Saint-Pierre. Viability Theory, New Directions. Springer, 2011.

[2]

J.-P. Aubin, J. Lygeros, M. Quincampoix, S. Sastry, and N. Seube. Impulse differential inclusions: a viability approach to hybrid systems. IEEE Transaction on Automatic Control, 47: 2--20, 2002.

[3]

J.-P. Aubin, D. Pujal, and P. Saint-Pierre. Dynamic management of portfolios with transaction costs under tychastic uncertainty. Springer, 2005.

[4]

E. Bakker, L. Nyborg, and H. Pacejka. Tyre modelling for use in vehicle dynamics studies. SAE, 1987.

[5]

E. Frazzoli, M. Dahleh, and E. Feron. Maneuver-based motion planning for nonlinear systems with symmetries. IEEE Transactions on Robotics, 21(6): 1077--1091, 2005.

Digital Library

[6]

Y. Gao, T. Lin, F. Borrelli, E. Tseng, and D. Hrovat. Predictive control of autonomous ground vehicles with obstacle avoidance on slippery roads. Proceedings of DSCC, 2010.

[7]

A. Gray, Y. Gao, T. Lin, J. K. Hedrick, H. E. Tseng, and F. Borrelli. Predictive control for agile semi-autonomous ground vehicles using motion primitives. In American Control Conference (ACC), pages 4239--4244. IEEE, 2012.

[8]

L. Grüne and J. Pannek. Nonlinear Model Predictive Control. Springer London, 2011.

[9]

M. Herceg, M. Kvasnica, C. Jones, and M. Morari. Multi-Parametric Toolbox 3.0. In Proc. of the European Control Conference, pages 502--510, Zürich, Switzerland, July 17--19 2013. http://control.ee.ethz.ch/~mpt.

[10]

M. Kalisiak and M. van de Panne. Faster motion planning using learned local viability models. In International Conference on Robotics and Automation, pages 2700--2705, 2007.

[11]

E. Kerrigan and M. Maciejowski, J. Invariant sets for constrained nonlinear discrete-time systems with application to feasibility in model predictive control. In Conference on Decision and Control (CDC), pages 4951--4956, 2000.

[12]

I. Kitsios and J. Lygeros. Aerodynamic envelope computation for safe landing of the hl-20 personnel launch vehicle using hybrid control. In International Symposium on Intelligent Control, pages 231--236, 2005.

[13]

A. Liniger, A. Domahidi, and M. Morari. Optimization-based autonomous racing of 1:43 scale rc cars. Optimal Control Applications and Methods, pages 1--19, 2014.

[14]

J. Lygeros. On reachabiltiy and minimum cost optimal control. Automatica, 40: 917--927, 2004.

Digital Library

[15]

J. Lygeros, K. H. Johansson, S. N. Simic, J. Zhang, and S. S. Sastry. Dynamical properties of hybrid automata. IEEE Transaction on Automatic Control, 48: 2--17, 2003.

[16]

K. Margellos and J. Lygeros. Viable set computation for hybrid systems. Nonlinear Analysis: Hybrid Systems, 10: 45--62, 2013.

[17]

R. Moitie and N. Seube. Guidance algorithms for uuvs obstacle avoidance systems. In OCEANS 2000 MTS/IEEE Conference and Exhibition, volume 3, pages 1853--1860. IEEE, 2000.

[18]

D. Panagou, K. Margellos, S. Summers, J. Lygeros, and K. J. Kyriakopoulos. A viability approach for the stabilization of an underactuated underwater vehicle in the presence of current disturbances. In Conference on Decision and Control (CDC), pages 8612--8617, 2005.

[19]

P. Saint-Pierre. Approximation of the viability kernel. Applied Mathematics and Optimization, pages 187--209, 1994.

Digital Library

[20]

N. Seube, R. Moitie, and G. Leitmann. Aircraft take-off in windshear: a viability approach. Set-Valued Analysis, 8(1--2): 163--180, 2000.

[21]

A. Tinka, S. Diemer, L. Madureira, E. B. Marques, J. B. De Sousa, R. Martins, J. Pinto, J. E. Da Silva, A. Sousa, P. Saint-Pierre, and A. M. Bayen. Viability-based computation of spatially constrained minimum time trajectories for an autonomous underwater vehicle: Implementation and experiments. In Conference on Decision and Control (CDC), pages 3603--3610, 2009.

Digital Library

[22]

T. A. Wood, P. Mohajerin Esfahani, and J. Lygeros. Hybrid modelling and reachability on autonomous rc-cars. In Analysis and Design of Hybrid Systems, pages 430--435, 2012.

Cited By

Zhang TSun YWang YLi BTian YWang F(2024)A Survey of Vehicle Dynamics Modeling Methods for Autonomous Racing: Theoretical Models, Physical/Virtual Platforms, and PerspectivesIEEE Transactions on Intelligent Vehicles10.1109/TIV.2024.33511319:3(4312-4334)Online publication date: Mar-2024
https://doi.org/10.1109/TIV.2024.3351131
Betz JBetz TFent FGeisslinger MHeilmeier AHermansdorfer LHerrmann THuch SKarle PLienkamp MLohmann BNobis FÖgretmen LRowold MSauerbeck FStahl TTrauth RWerner FWischnewski A(2023)TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous ChallengeJournal of Field Robotics10.1002/rob.2215340:4(783-809)Online publication date: 12-Jan-2023
https://doi.org/10.1002/rob.22153
Rowold MÖgretmen LKerbl TLohmann B(2022)Efficient Spatiotemporal Graph Search for Local Trajectory Planning on Oval Race TracksActuators10.3390/act1111031911:11(319)Online publication date: 3-Nov-2022
https://doi.org/10.3390/act11110319
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Index Terms

A viability approach for fast recursive feasible finite horizon path planning of autonomous RC cars
1. Applied computing
  1. Physical sciences and engineering
2. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Model verification and validation

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Published In

cover image ACM Conferences

HSCC '15: Proceedings of the 18th International Conference on Hybrid Systems: Computation and Control

April 2015

321 pages

ISBN:9781450334334

DOI:10.1145/2728606

Program Chairs:
Antoine Girard
Université Joseph Fourier, Grenoble, France
,
Sriram Sankaranarayanan
University of Colorado at Boulder

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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IEEE-CSS: Control Systems Society
SIGBED: ACM Special Interest Group on Embedded Systems

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 April 2015

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HSCC '15

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HSCC '15: 18th International Conference on Hybrid Systems: Computation and Control

April 14 - 16, 2015

Washington, Seattle

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Overall Acceptance Rate 153 of 373 submissions, 41%

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Cited By

Zhang TSun YWang YLi BTian YWang F(2024)A Survey of Vehicle Dynamics Modeling Methods for Autonomous Racing: Theoretical Models, Physical/Virtual Platforms, and PerspectivesIEEE Transactions on Intelligent Vehicles10.1109/TIV.2024.33511319:3(4312-4334)Online publication date: Mar-2024
https://doi.org/10.1109/TIV.2024.3351131
Betz JBetz TFent FGeisslinger MHeilmeier AHermansdorfer LHerrmann THuch SKarle PLienkamp MLohmann BNobis FÖgretmen LRowold MSauerbeck FStahl TTrauth RWerner FWischnewski A(2023)TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous ChallengeJournal of Field Robotics10.1002/rob.2215340:4(783-809)Online publication date: 12-Jan-2023
https://doi.org/10.1002/rob.22153
Rowold MÖgretmen LKerbl TLohmann B(2022)Efficient Spatiotemporal Graph Search for Local Trajectory Planning on Oval Race TracksActuators10.3390/act1111031911:11(319)Online publication date: 3-Nov-2022
https://doi.org/10.3390/act11110319
Betz JZheng HLiniger ARosolia UKarle PBehl MKrovi VMangharam R(2022)Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle RacingIEEE Open Journal of Intelligent Transportation Systems10.1109/OJITS.2022.31815103(458-488)Online publication date: 2022
https://doi.org/10.1109/OJITS.2022.3181510
Zang ZTumu RBetz JZheng HMangharam R(2022)Winning the 3rd Japan Automotive AI Challenge - Autonomous Racing with the Autoware.Auto Open Source Software Stack2022 IEEE Intelligent Vehicles Symposium (IV)10.1109/IV51971.2022.9827162(1757-1764)Online publication date: 5-Jun-2022
https://doi.org/10.1109/IV51971.2022.9827162
Jang ILee DLee SKim H(2021)Robust and Recursively Feasible Real-Time Trajectory Planning in Unknown Environments2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS51168.2021.9636048(1434-1441)Online publication date: 27-Sep-2021
https://doi.org/10.1109/IROS51168.2021.9636048
Vrajitoru D(2021)Hybrid Car Trajectory by Genetic Algorithms with Non-Uniform Key FramingAdvances in Artificial Intelligence and Applied Cognitive Computing10.1007/978-3-030-70296-0_29(369-380)Online publication date: 15-Oct-2021
https://doi.org/10.1007/978-3-030-70296-0_29
Vrajitoru DAuger AStützle T(2019)Trajectory optimization for car races using genetic algorithmsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3326792(85-86)Online publication date: 13-Jul-2019
https://dl.acm.org/doi/10.1145/3319619.3326792
Liniger ALygeros J(2019)Real-Time Control for Autonomous Racing Based on Viability TheoryIEEE Transactions on Control Systems Technology10.1109/TCST.2017.277290327:2(464-478)Online publication date: Mar-2019
https://doi.org/10.1109/TCST.2017.2772903
Vrajitoru D(2018)Global to Local for Path Decision using Neural NetworksProceedings of the International Conference on Pattern Recognition and Artificial Intelligence10.1145/3243250.3243269(117-123)Online publication date: 15-Aug-2018
https://dl.acm.org/doi/10.1145/3243250.3243269
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