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Moving Obstacle Avoidance: a Data-Driven Risk-Aware Approach

Wei, Skylar X. and Dixit, Anushri and Tomar, Shashank and Burdick, Joel W. (2022) Moving Obstacle Avoidance: a Data-Driven Risk-Aware Approach. IEEE Control Systems Letters, 7 . pp. 289-294. ISSN 2475-1456. doi:10.1109/lcsys.2022.3181191.

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This letter proposes a new structured method for a moving agent to predict the paths of dynamically moving obstacles and avoid them using a risk-aware model predictive control (MPC) scheme. Given noisy measurements of the a priori unknown obstacle trajectory, a bootstrapping technique predicts a set of obstacle trajectories. The bootstrapped predictions are incorporated in the MPC optimization using a risk-aware methodology so as to provide probabilistic guarantees on obstacle avoidance. We validate our methods using simulations of a multi-rotor drone that avoids various moving obstacles.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
Wei, Skylar X.0000-0002-6336-9433
Dixit, Anushri0000-0002-9698-2189
Burdick, Joel W.0000-0002-3091-540X
Additional Information:© 2022 IEEE. Manuscript received 21 March 2022; revised 21 May 2022; accepted 24 May 2022. Date of publication 8 June 2022; date of current version 15 July 2022. Recommended by Senior Editor V. Ugrinovskii.
Subject Keywords:Predictive control for linear systems, Stochastic optimal control, Uncertain systems, Robotics.
Record Number:CaltechAUTHORS:20220614-222127000
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Official Citation:S. X. Wei, A. Dixit, S. Tomar and J. W. Burdick, "Moving Obstacle Avoidance: A Data-Driven Risk-Aware Approach," in IEEE Control Systems Letters, vol. 7, pp. 289-294, 2023, doi: 10.1109/LCSYS.2022.3181191
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115146
Deposited By: George Porter
Deposited On:14 Jun 2022 22:07
Last Modified:02 Aug 2022 16:56

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