Trajectory Planning for Hybrid Unmanned Aerial Underwater Vehicles with Smooth Media Transition
This addresses the gap in high-level intelligence for hybrid vehicles, which is incremental as it applies an existing method variant to a new domain.
The paper tackles the problem of high-level motion planning for Hybrid Unmanned Aerial Underwater Vehicles, proposing a trajectory planning approach that enables collision avoidance and smooth transitions between aerial and aquatic media, with simulated results demonstrating applicability in obstacle-filled environments.
In the last decade, a great effort has been employed in the study of Hybrid Unmanned Aerial Underwater Vehicles, robots that can easily fly and dive into the water with different levels of mechanical adaptation. However, most of this literature is concentrated on physical design, practical issues of construction, and, more recently, low-level control strategies. Little has been done in the context of high-level intelligence, such as motion planning and interactions with the real world. Therefore, we proposed in this paper a trajectory planning approach that allows collision avoidance against unknown obstacles and smooth transitions between aerial and aquatic media. Our method is based on a variant of the classic Rapidly-exploring Random Tree, whose main advantages are the capability to deal with obstacles, complex nonlinear dynamics, model uncertainties, and external disturbances. The approach uses the dynamic model of the \hydrone, a hybrid vehicle proposed with high underwater performance, but we believe it can be easily generalized to other types of aerial/aquatic platforms. In the experimental section, we present simulated results in environments filled with obstacles, where the robot is commanded to perform different media movements, demonstrating the applicability of our strategy.