Investigating the Capabilities of Miniature Autonomous Surface Vehicles through a Game of Pong

Abstract

Monitoring underwater weather is important to understanding and maintaining the health of large water bodies, such as rivers, lakes, and oceans. Leveraging autonomous surface vehicles (ASVs) with on-board sensing capabilities can provide more useful and consistent information that captures the state of these water bodies. While large ASVs are currently in development at the ScalAR Lab, we simulate the performances of similar, yet smaller boats, miniature ASVs (mASVs). Investigating the performance of planning algorithms on board these mASVs can allow for a deeper understanding of the capabilities of coordinated tasks with teams of ASVs. In this project, a real-time planning algorithm for the mASVs is developed in Python and implemented to run in the Multi-Robot Tank (MR tank), to simulate a game similar to pong. In this game, miniature boats deflect an object between one another with a given set of boundaries. With the development of these planning algorithms, these concepts can be implemented into completing tasks for teams of larger ASVs. The implications may include the coordination of these vehicles in completing their given objectives, such as transferring waste materials collected from the surface of the river between multiple ASVs.