Coverage of an Environment Using Energy-Constrained Unmanned Aerial Vehicles

TL;DR

This paper presents an optimal algorithm for coordinating UAVs and UGVs to efficiently cover an environment considering energy constraints, recharging strategies, and different UAV types, reducing the problem to a known NP-hard problem.

Contribution

It introduces a new variant of the area coverage problem involving energy-constrained UAVs and UGVs with recharging, and provides an optimal solution via reduction to GTSP.

Findings

The algorithm finds optimal coverage paths for UAVs and UGVs.

Simulation and experiments validate the effectiveness of the approach.

The method accommodates different UAV types and recharging strategies.

Abstract

We study the problem of covering an environment using an Unmanned Aerial Vehicle (UAV) with limited battery capacity. We consider a scenario where the UAV can land on an Unmanned Ground Vehicle (UGV) and recharge the onboard battery. The UGV can also recharge the UAV while transporting the UAV to the next take-off site. We present an algorithm to solve a new variant of the area coverage problem that takes into account this symbiotic UAV and UGV system. The input consists of a set of boustrophedon cells -- rectangular strips whose width is equal to the field-of-view of the sensor on the UAV. The goal is to find a coordinated strategy for the UAV and UGV that visits and covers all cells in minimum time, while optimally finding how much to recharge, where to recharge, and when to recharge the battery. This includes flight time for visiting and covering all cells, recharging time, as well as the take-off and landing times. We show how to reduce this problem to a known NP-hard problem, Generalized Traveling Salesperson Problem (GTSP). Given an optimal GTSP solver, our approach finds the optimal coverage paths for the UAV and UGV. Our formulation models multi-rotor UAVs as well as hybrid UAVs that can operate in fixed-wing and Vertical Take-off and Landing modes. We evaluate our algorithm through simulations and proof-of-concept experiments.