Planning under uncertainty and constraints for teams of autonomous agents

Abstract

One of the main advantages of unmanned, autonomous vehicles is their potential use in dangerous situations, such as victim search and rescue in the aftermath of an urban disaster. Unmanned vehicles can complement human first responders by performing tasks that do not require human expertise (e.g., communication) and supplement them by providing capabilities a human first responder would not have immediately available (e.g., aerial surveillance). However, for unmanned vehicles to work seamlessly and unintrusively with human responders, a high degree of autonomy and planning is necessary. In particular, the unmanned vehicles should be able to account for the dynamic nature of their operating environment, the uncertain nature of their tasks and outcomes, and the risks that are inherent in working in such a situation. This thesis therefore addresses the problem of planning under uncertainty in the presence of risk. This work formulates the planning problem as a Markov Decision Process with constraints, and offers a formal definition for the notion of "risk". Then, a fast and computationally efficient solution is proposed. Next, the complications that arise when planning for large teams of unmanned vehicles are considered, and a decentralized approach is investigated and shown to be efficient under some assumptions. However some of these assumptions place restrictions - specifically on the amount of risk each agent can take. These restrictions hamper individual agents' ability to adapt to a changing environment. Hence a consensus-based approach that allows agents to take more risk is introduced and shown to be effective in achieving high reward. Finally, some experimental results are presented that validate the performance of the solution techniques proposed.