Risk-Aware Planning and Control in Extreme Environments

Citation

Dixit, Anushri C. (2023) Risk-Aware Planning and Control in Extreme Environments. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/xv2b-tj24. https://resolver.caltech.edu/CaltechTHESIS:02082023-223824752

Abstract

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.

Thesis Files