Abstract
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.”
NETL
NSTL
IEEE