首页|Stanford University Details Findings in Robotics and Automation (Risk-averse Trajectory Optimization Via Sample Average Approximation)
Stanford University Details Findings in Robotics and Automation (Risk-averse Trajectory Optimization Via Sample Average Approximation)
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IEEE
New research on Robotics - Robotics and Automation is the subject of a report. According to news originating from Stanford, California, by NewsRx correspondents, research stated, “Trajectory optimization under uncertainty underpins a wide range of applications in robotics. However, existing methods are limited in terms of reasoning about sources of epistemic and aleatoric uncertainty, space and time correlations, nonlinear dynamics, and non-convex constraints.” Financial support for this research came from NASA University Leadership Initiative. Our news journalists obtained a quote from the research from Stanford University, “In this work, we first introduce a continuous-time planning formulation with an average-value-at-risk constraint over the entire planning horizon. Then, we propose a sample-based approximation that unlocks an efficient and generalpurpose algorithm for risk-averse trajectory optimization. We prove that the method is asymptotically optimal and derive finite-sample error bounds.” According to the news editors, the research concluded: “Simulations demonstrate the high speed and reliability of the approach on problems with stochasticity in nonlinear dynamics, obstacle fields, interactions, and terrain parameters.”
StanfordCaliforniaUnited StatesNorth and Central AmericaRobotics and AutomationRoboticsStanford University
NETL
NSTL
IEEE
