Information-driven Path Planning for Hybrid Aerial Underwater Vehicles

TL;DR

This paper introduces a novel RAST algorithm for hybrid aerial underwater vehicles that enhances adaptive sampling efficiency and path planning in complex 3D environments, outperforming existing algorithms in speed and stability.

Contribution

The paper proposes a new RAST algorithm combining tournament-based sampling, heuristic search, and RRT framework for improved adaptive sampling and path planning of HAUVs.

Findings

RAST outperforms RIGT and PSO in optimization performance.

RAST achieves faster solution speed.

RAST demonstrates better stability in simulations.

Abstract

This paper presents a novel Rapidly-exploring Adaptive Sampling Tree (RAST) algorithm for the adaptive sampling mission of a hybrid aerial underwater vehicle (HAUV) in an air-sea 3D environment. This algorithm innovatively combines the tournament-based point selection sampling strategy, the information heuristic search process and the framework of Rapidly-exploring Random Tree (RRT) algorithm. Hence can guide the vehicle to the region of interest to scientists for sampling and generate a collision-free path for maximizing information collection by the HAUV under the constraints of environmental effects of currents or wind and limited budget. The simulation results show that the fast search adaptive sampling tree algorithm has higher optimization performance, faster solution speed and better stability than the Rapidly-exploring Information Gathering Tree (RIGT) algorithm and the particle swarm optimization (PSO) algorithm.