PhD Defense: Robot Planning in Adversarial Environments Using Tree Search Techniques

One of the main advantages of robots is that they can be used in environments that are dangerous for humans. Robots can not only be used for tasks in known and safe areas but also in environments that may have adversaries. When planning the robot's actions in such scenarios, we have to consider the outcomes of a robot's actions based on the actions taken by the adversary, as well as the information available to the robot and the adversary. The goal of this dissertation is to design planning strategies that improve the robot's performance in adversarial environments. Specifically, we study how the availability of information affects the planning process and the outcome. We also study how to improve computational efficiency by exploiting the structural properties of the underlying setting.
We adopt a game-theoretic formulation and study two scenarios: adversarial active target tracking and reconnaissance in environments with adversaries. A conservative approach is to plan the robot's action assuming a worst-case adversary with complete knowledge of the robot's state and objective. We start with such a "symmetric" information game for the adversarial target tracking scenario with noisy sensing. By using the properties of the Kalman filter, we design a pruning strategy to improve the efficiency of a tree search algorithm. We then investigate the performance limits of the asymmetric version where the adversary can inject false sensing data. We then study a reconnaissance scenario where the robot and the adversary have symmetric information. We design an algorithm that allows a robot to scan more areas while avoid being detected by the adversary. The symmetric adversarial model may yield too conservative plans when the adversary may not have the same information as the robot. Furthermore, the information available to the adversary may change during execution. We then investigate the dynamic version of this asymmetric information game and show how much the robot can exploit the asymmetry in information using tree search techniques. Specifically, we study scenarios where the information available to the adversary changes during execution. We devise a new algorithm for this asymmetric information game with theoretical performance guarantees and evaluate those approaches through experiments. We use qualitative examples to show how the new algorithm can outperform symmetric minimax and use quantitative experiments to show how much the improvement is.
Examining Committee:
Chair: Dr. Pratap Tokekar Dean's rep: Dr. Jeffrey W. Herrmann Members: Dr. Huaishu Peng Dr. Dinesh Manocha Dr. Edmund H. Durfee