3D-Map Assisted UAV Trajectory Design Under Cellular Connectivity Constraints

TL;DR

This paper introduces a low-complexity, graph-based method for designing UAV trajectories that ensure continuous cellular connectivity by utilizing 3D environment maps and radio propagation models.

Contribution

It presents a novel approach combining 3D mapping and convexity-based algorithms for efficient UAV trajectory planning under connectivity constraints.

Findings

Achieves near-optimal trajectory solutions with reduced computational complexity.

Utilizes a coverage map derived from 3D environment and radio models.

Demonstrates effectiveness through simulations or experiments.

Abstract

The enabling of safe cellular controlled unmanned aerial vehicle (UAV) beyond visual line of sight is expected to open important future opportunities in the area of transportation, goods delivery, and system monitoring. A key challenge in this area lies in the design of trajectories which, while allowing the completion of the UAV mission, can guarantee reliable cellular connectivity all along the path. Previous approaches in this domain have considered simplistic propagation model assumptions (e.g. Line of Sight based) or more advanced models but with computationally demanding optimized solutions. In this paper, we propose a novel approach for trajectory design using a coverage map that can be obtained with a combination of 3D map of the environment and radio propagation models. Leveraging on the convexity of sub-regions within the coverage map, we propose a low-complexity graph based algorithm which is shown to achieve quasi-optimal performance at a fraction of the computational cost of known optimal methods.