Trajectory Optimization for Cellular-Enabled UAV with Connectivity and Battery Constraints

TL;DR

This paper presents an efficient polynomial-time algorithm for optimal UAV trajectory planning that accounts for connectivity, battery constraints, and payload objectives, outperforming existing methods.

Contribution

It introduces a novel polynomial-time algorithm converting the path planning problem into a two-level graph search, ensuring optimal trajectories under multiple constraints.

Findings

The proposed algorithm guarantees optimal trajectories in polynomial time.

Compared to existing algorithms, it offers better performance and computational efficiency.

It can optimize for energy consumption and payload weight alongside connectivity and battery constraints.

Abstract

We address the path planning problem for a cellular-enabled unmanned aerial vehicle (UAV) considering both connectivity and battery constraints. The UAV's mission is to expeditiously transport a payload from an initial point to a final point, while persistently keeping the connection with a base station and complying with its battery limit. At a charging station, the UAV's depleted battery can be swapped with a completely charged one. Our primary contribution lies in proposing an algorithm that outputs an optimal UAV trajectory with polynomial computational complexity, by converting the problem into an equivalent two-level graph-theoretic shortest path search problem. We compare our algorithm with several existing algorithms with respect to performance and computational complexity, and show that only our algorithm outputs an optimal UAV trajectory in polynomial time. Furthermore, we consider other objectives of minimizing the UAV energy consumption and of maximizing the deliverable payload weight, and propose algorithms that output an optimal UAV trajectory in polynomial time.