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...
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| Vydané v: | IEEE robotics and automation letters Ročník 4; číslo 3; s. 2276 - 2281 |
|---|---|
| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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Piscataway
IEEE
01.07.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2377-3766, 2377-3766 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Okamoto, Kazuhide Tsiotras, Panagiotis |
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| SubjectTerms | 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 |
| Title | Optimal Stochastic Vehicle Path Planning Using Covariance Steering |
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