Multi-Domain Informative Coverage Path Planning for a Hybrid Aerial Underwater Vehicle in Dynamic Environments

Abstract

Hybrid aerial underwater vehicles (HAUV) are a new frontier for vehicles. They can operate both underwater and aerially, providing enormous potential for a wide range of scientific explorations. Informative path planning is essential to vehicle autonomy. However, covering an entire mission region is a challenge to HAUVs because of the possibility of a multidomain environment. This paper presents an informative trajectory planning framework for planning paths and generating trajectories for HAUVs performing multidomain missions in dynamic environments. We introduce the novel heuristic generalized extensive neighborhood search GLNS–k-means algorithm that uses k-means to cluster information into several sets; then through the heuristic GLNS algorithm, it searches the best path for visiting these points, subject to various constraints regarding path budgets and the motion capabilities of the HAUV. With this approach, the HAUV is capable of sampling and focusing on regions of interest. Our method provides a significantly more optimal trajectory (enabling collection of more information) than ant colony optimization (ACO) solutions. Moreover, we introduce an efficient online replanning scheme to adapt the trajectory according to the dynamic obstacles during the mission. The proposed replanning scheme based on KD tree enables significantly shorter computational times than the scapegoat tree methods.