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Risk-Aware Planning and Control in Extreme Environments


Dixit, Anushri C. (2023) Risk-Aware Planning and Control in Extreme Environments. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/xv2b-tj24.


Safety-critical control and planning for autonomous systems operating in unstructured environments is a challenging problem must be addressed as autonomous vehicles, surgical robots, and autonomous industrial robots become more pervasive. This thesis addresses some of the issues in safety critical autonomy by introducing new techniques for computationally tractable and efficient safety-critical control. The approach developed in this thesis arises from taking a deeper look at two questions: 1) How can we obtain better uncertainty quantification of the disturbances that affect autonomous systems either as a result of unmodeled changes in the environment or due to sensor imperfections? 2) Given richer uncertainty quantification techniques, how do incorporate the diverse uncertainty descriptions into the control and planning framework without sacrificing the tractability and efficiency of existing approaches?

I address the above two questions by developing risk-aware control and planning techniques for traversal of a mobile robot over static but extreme terrain and in the presence of dynamic obstacles. We first look at algorithms for risk-aware terrain assessment, and extensively test them on wheeled and legged robots that were deployed in subterranean tunnel, urban, and cave environments for search and rescue operations in the DARPA Subterranean Challenge. I then present a theory for risk-aware model predictive control in static environments and in the presence of dynamic obstacles. Coherent risk measures are applied to this planning and control framework in order to account for diverse uncertainty descriptions. Computationally tractable reformulations of the optimal control problem are realized through constraint tightening techniques.

I then investigate algorithms for uncertainty assessment and prediction of apriori unknown, dynamic obstacles using data-driven techniques. We use a technique from signal processing literature called Singular Spectrum Analysis for making linear predictions of dynamic obstacles. The obstacle motion predictions are equipped with error predictions to account for the uncertainty in the sensing heuristically using bootstrapping techniques. We use a statistical tool, Adaptive Conformal Inference, to further calibrate the heuristic error prediction online to obtain true uncertainty prediction while using nonstationary data to analyze the performance of the data-driven predictor. These techniques provide reactive, real-time, risk-aware obstacle avoidance in dynamic environments.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Robotics, risk-aware planning, stochastic control
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Control and Dynamical Systems
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Burdick, Joel Wakeman
Thesis Committee:
  • Murray, Richard M. (chair)
  • Ames, Aaron D.
  • Chung, Soon-Jo
  • Mazumdar, Eric V.
  • Burdick, Joel Wakeman
Defense Date:2 February 2023
Funding AgencyGrant Number
Defense Advanced Research Projects Agency (DARPA)UNSPECIFIED
Record Number:CaltechTHESIS:02082023-223824752
Persistent URL:
Related URLs:
URLURL TypeDescription Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge adapted for ch.3: STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation. Article adapted for ch.3 adapted for ch.5: Risk-Sensitive Motion Planning using Entropic Value-at-Risk Bounds for Coherent Risk Measures: Applications to Policy Synthesis and Verification adapted for ch.4: Distributionally Robust Model Predictive Control With Total Variation Distance adapted for ch.6: Moving Obstacle Avoidance: A Data-Driven Risk-Aware Approach adapted for ch.5: Risk-Averse Receding Horizon Motion Planning Stochastic Shortest Path Planning adapted for ch.6: Adaptive Conformal Prediction for Motion Planning among Dynamic Agents Predictive Proprioception for Agile Failure Event Detection in Robotic Exploration of Extreme Terrains Kinematics of Tracked Vehicles via the Power Dissipation Method
Dixit, Anushri C.0000-0002-9698-2189
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:15104
Deposited By: Anushri Dixit
Deposited On:23 Feb 2023 19:45
Last Modified:18 Apr 2023 03:01

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