Handover-Aware URLLC UAV Trajectory Planning: A Continuous-Time Trajectory Optimization via Graphs of Convex Sets

TL;DR

This paper presents a novel continuous-time trajectory optimization method for cellular-connected UAVs that minimizes handovers and flight time while maintaining URLLC, using a graph of convex sets approach.

Contribution

It introduces a GCS-based optimization framework that jointly plans UAV trajectories and base station associations to reduce handovers and improve communication reliability.

Findings

The method effectively reduces handovers compared to baseline approaches.

Simulations show a trade-off between fewer handovers and flight efficiency.

The approach maintains URLLC connectivity during UAV flights.

Abstract

In this paper, we study a cellular-connected unmanned aerial vehicle (UAV) which aims to fly between two predetermined locations while maintaining ultra-reliable low-latency communications (URLLC) for command-and-control (C2) links with terrestrial base stations (BSs). Long-range flights often trigger frequent inter-cell handovers, which may introduce delays and synchronization overhead. We jointly optimize the continuous trajectory and BS association to minimize handovers, path length, and flying time, subject to communication reliability and kinematic constraints. To address this problem, we reformulate it as an optimization based on the graph of convex sets (GCS). First, the URLLC requirement is translated into spatially feasible regions in the flight plane for each BS. And an intersection graph is constructed including the start and goal points. Each graph node is associated with a smooth and dynamically feasible trajectory segment. The trajectory is parameterized in space by B\'ezier curves and in time by a monotonic B\'ezier scaling, together with convex constraints that ensure continuity and enforce speed bounds. Next, we impose unit-flow constraints to enforce a single path, and by coupling the resulting binary edge-selection variables with the convex constraints, we obtain a mixed-integer convex program (MICP). Applying a convex relaxation and rounding to the mixed-integer convex program produces nearly globally optimal routes, and a final refinement yields smooth, dynamically feasible trajectories. Simulations verify that the method preserves URLLC connectivity while achieving a clear trade-off between fewer handovers and flight efficiency.