Unmanned Aerial Vehicle Planning

Robert Burns once wrote: “The best-laid plans of mice and men oft go astray.”  Nowhere is this statement truer than when planning missions in an uncertain, adversarial environment.  Despite our best efforts and hundreds of years of experience, planning is still an arduous task whose results often dictate the outcome before the first action is ever taken.  However, as Burns points out, even the best laid plans must be able to adapt to unforeseen circumstances to ensure that they succeed.  The overall goal of this project is to develop new technologies to dynamically control and coordinate multiple autonomous vehicles so they can accomplish their missions while the enemy is attempting to deny them access and prevent them from communicating.

Our approach to addressing this complex problem is to expand upon our previous task allocation results by augmenting dynamic, distributed constraint reasoning with machine learning techniques and adaptive response strategies.  By combining these technologies, we propose to build a system that can 1) develop robust, adaptable mission plans 2) exploit knowledge learned through prior interactions with our adversary, and 3) autonomously and dynamically alter its behavior during mission execution to improve the likelihood of a successful outcome.