A Simulation Based Framework for Discovering Planning Logic for Autonomous Unmanned Surface Vehicles

This paper describes a computational framework for automatically synthesizing planning logic for unmanned surface vehicles (USVs). The basic idea behind our approach is as follows. The USV explores the virtual environment by randomly trying different moves. USV moves are simulated in the virtual environment and evaluated based on their ability to make progress towards the mission goal. If a successful action is identified as a part of the random exploration, then this action is integrated into the logic driving the USV. This approach has been utilized for automatically generating planning logic for USVs. The planning logic is represented as a decision tree which consists of high-level controllers as building blocks, conditionals and other program constructs. We used strongly-typed GP-based evolutionary framework for automatic generation of planning logic for blocking the advancement of a computer-driven intruder boat toward a valuable target. Our results show that a genetic programming based synthesis framework is capable of generating decision trees expressing useful logic for blocking the advancements of an enemy boat.