Obstacle avoidance for unmanned wing-in-ground vehicles

The use of Wing-In-Ground (WIG) vehicles marks a significant evolution in autonomous transportation, bridging the gap between aerial and maritime domains and combining maritime vessels' efficiency with aircraft speed and flexibility. These vehicles navigate the complex interface between sea and air, requiring sophisticated navigational strategies to manage their unique dynamics. Central to their deployment in defence and security applications is the ability to rapidly deploy and intervene at sea without infrastructure or launch vehicles for departure and landing. This paper presents an obstacle avoidance framework for Unmanned WIG Vehicles (UWVs) that integrates advanced image segmentation techniques, drawing upon comprehensives datasets for obstacle detection and avoidance.

The datasets chosen for training and testing encompass a wide range of maritime scenarios, including lakes, rivers, and seas, serve as the foundation for this study. It offers various scene types, obstacle classifications, and environmental conditions.

The study of different image segmentation CNNs represents a pivotal step towards robust autonomy in UWVs, particularly in defence and security, where reliability and precision are paramount. The methodology presented may establish the foundation for an obstacle avoidance system that improves the operational efficiency of UWVs while enhancing their safety and providing a more accurate and collision-free navigation through the dynamically changing maritime environments.