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English to Spanish: Optimal Stochastic Vehicle Path Planning Using Covariance Steering General field: Tech/Engineering Detailed field: Automation & Robotics
Source text - English Optimal Stochastic Vehicle Path Planning Using Covariance Steering
by Kazuhide Okamoto and Panagiotis Tsiotras
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.
Translation - Spanish Planificación óptima de la trayectoria estocástica de un vehículo utilizando la conducción de covarianza
por Kazuhide Okamoto y Panagiotis Tsiotras
Resumen:
Esta carta aborda el problema de la planificación de la trayectoria de un vehículo en presencia de obstáculos e incertidumbres, un problema fundamental de la robótica. Si bien se han propuesto varios algoritmos de planificación de trayectorias a lo largo de los años, muchos de ellos han tratado solo con entornos deterministas o con incertidumbre de lazo abierto, es decir, la incertidumbre del estado del sistema no está controlada y, por lo general, aumenta con el tiempo debido a perturbaciones exógenas. Esto puede resultar en trayectorias nominales potencialmente conservadoras. La solución típica para lidiar con las perturbaciones y reducir la incertidumbre es utilizar un controlador de bajo nivel con retroalimentación. Proponemos la premisa de que, si un planificador de trayectorias puede considerar la evolución de lazo cerrado de la incertidumbre del sistema, puede llevar a trayectorias menos conservadoras, pero todavía factibles. Con este objetivo, en esta carta, desarrollamos un enfoque que se basa en una conducción de covarianza óptima, que conduce explícitamente la covarianza de estado para sistemas lineales estocásticos. Verificamos el marco propuesto utilizando simulaciones numéricas extensas.
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Years of experience: 3. Registered at ProZ.com: Jan 2013.
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