Online Ride-Hitching in UAV Travelling

TL;DR

This paper studies how UAVs can save travel distance by hitching rides with public trucks, proposing optimal offline solutions and online algorithms with performance guarantees in real-time scenarios.

Contribution

It introduces the first theoretical framework for online UAV truck hitching, including optimal offline algorithms and online strategies with performance bounds.

Findings

Optimal offline shortest-path algorithm for known truck trips

Online algorithms with performance ratios approaching lower bounds

Proposed algorithms improve UAV travel efficiency in real-time scenarios

Abstract

The unmanned aerial vehicle (UAV) has emerged as a promising solution to provide delivery and other mobile services to customers rapidly, yet it drains its stored energy quickly when travelling on the way and (even if solar-powered) it takes time for charging power on the way before reaching the destination. To address this issue, existing works focus more on UAV's path planning with designated system vehicles providing charging service. However, in some emergency cases and rural areas where system vehicles are not available, public trucks can provide more feasible and cost-saving services and hence a silver lining. In this paper, we explore how a single UAV can save flying distance by exploiting public trucks, to minimize the travel time of the UAV. We give the first theoretical work studying online algorithms for the problem, which guarantees a worst-case performance. We first consider the offline problem knowing future truck trip information far ahead of time. By delicately transforming the problem into a graph satisfying both time and power constraints, we present a shortest-path algorithm that outputs the optimal solution of the problem. Then, we proceed to the online setting where trucks appear in real-time and only inform the UAV of their trip information some certain time $\Delta t$ beforehand. As a benchmark, we propose a well-constructed lower bound that an online algorithm could achieve. We propose an online algorithm MyopicHitching that greedily takes truck trips and an improved algorithm $\Delta t$-Adaptive that further tolerates a waiting time in taking a ride. Our theoretical analysis shows that $\Delta t$-Adaptive is asymptotically optimal in the sense that its ratio approaches the proposed lower bounds as $\Delta t$ increases.