Motion Planning for USV using Classification Capabilities

This paper presents an automatic method to acquire, identify, and track obstacles from an Unmanned Surface Vehicle (USV) location in marine environments using 2D Commercial Of The Shelf (COTS) video sensors, and analyzing video streams as input. The guiding line of this research is to develop real-time automatic identification and tracking abilities in marine environment with COTS sensors. The output of this algorithm provides obstacle’s location in x-y coordinates. The ability to recognize and identify obstacles becomes more essential for USV’s autonomous capabilities, such as obstacle avoidance, decision modules, and other Artificial Intelligence (AI) abilities using low cost sensors. Our algorithm is not based on obstacles characterization. Algorithm performances were tested in various scenarios with real-time USV’s video streams, indicating that the algorithm can be used for real-time applications with high success rate and fast time computation.