Distributionally Robust Risk Map for Learning-Based Motion Planning and Control: A Semidefinite Programming Approach

In this article, we propose a novel safety specification tool, called the distributionally robust risk map (DR-risk map), for a mobile robot operating in a learning-enabled environment. Given the robot's position, the map aims to reliably assess the conditional value-at-risk (CVaR) of collision...

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Bibliographic Details
Published in:	IEEE transactions on robotics Vol. 39; no. 1; pp. 1 - 20
Main Authors:	Hakobyan, Astghik, Yang, Insoon
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.02.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Collision avoidance Computational modeling Control methods Errors Gaussian process Learning Motion control Motion planning Neural networks Optimization Planning Predictive control probability distribution Robot dynamics Robot kinematics Robot sensing systems Robots Robustness Safety Semidefinite programming Upper bounds
ISSN:	1552-3098, 1941-0468
Online Access:	Get full text
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