Optimal Stochastic Vehicle Path Planning Using Covariance Steering

This letter addresses the problem of vehicle path planning in the presence of obstacles and uncertainties, a fundamental robotics problem. While several path planning algorithms have been proposed over the years, many of them have dealt with only deterministic environments or with only open-loop unc...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE robotics and automation letters Jg. 4; H. 3; S. 2276 - 2281
Hauptverfasser: Okamoto, Kazuhide, Tsiotras, Panagiotis
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.07.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Aerodynamics
Algorithms
Computer simulation
Covariance
Disturbances
Feedback control
formal methods in robotics and automation
Gaussian distribution
Linear systems
Motion and path planning
Optimal control
Path planning
Planning
probability and statistical methods
Programming
Robotics
Robots
robust/adaptive control of robotic systems
Steering
Uncertainty
ISSN:2377-3766, 2377-3766
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This letter addresses the problem of vehicle path planning in the presence of obstacles and uncertainties, a fundamental robotics problem. While several path planning algorithms have been proposed over the years, many of them have dealt with only deterministic environments or with only open-loop uncertainty, i.e., the uncertainty of the system state is not controlled and, typically, increases with time because of exogenous disturbances. This may lead to potentially conservative nominal paths. The typical approach to deal with disturbances and reduce uncertainty is to use a lower level feedback controller. We advocate the premise that, if a path planner can consider the closed-loop evolution of the system uncertainty, it can lead to less conservative, but still feasible, paths. To this end, in this letter, we develop an approach that is based on optimal covariance steering, which explicitly steers the state covariance for stochastic linear systems. We verify the proposed framework using extensive numerical simulations.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2019.2901546