Trajectory Planning of an Aerial-Underwater Hybrid Vehicle Based on Heuristics for Energy Efficiency

Abstract

This work studies the trajectory planning of an unmanned hybrid aerial-underwater vehicle (HUAUV) called Hydrone, which is being developed by the Intelligent Robotics and Automation Group (NAUTEC) of the Federal University of Rio Grande (FURG). This study presents a new trajectory planning algorithm, based on closed-loop rapidly exploring random trees (CL-RRT). This algorithm is developed for an HUAUV and introduces two heuristics to improve its energy efficiency in hybrid tasks. Simulated experiments were carried out in 135 virtual scenarios, comparing three approaches: one without heuristics and two with the proposed heuristics. Simulated results demonstrate that using the heuristics can significantly reduce energy consumption and even improve the vehicle’s average speed during missions. In particular, in 95% of the scenarios, the lowest energy consumption was achieved by one of the two heuristic-based algorithms. This article concludes by summarizing the findings and identifying potential future research opportunities.