Optimizing Multi-UAV 3D Deployment for Energy-Efficient Sensing over Uneven Terrains

TL;DR

This paper presents a novel hierarchical heuristic framework combining genetic algorithms and particle swarm optimization to optimize multi-UAV deployment for energy-efficient sensing over uneven terrains, considering terrain-aware line of sight and safety constraints.

Contribution

It introduces a terrain-aware, bi-objective optimization method for multi-UAV deployment that effectively balances detection probability and energy consumption in complex terrains.

Findings

Detection probability improved by over 36% with 2-3 UAVs.

Average excess hover energy reduced by nearly 50%.

Significant performance gains over non-optimized schemes.

Abstract

In this work, we consider a multi-unmanned aerial vehicle (UAV) cooperative sensing system where UAVs are deployed to sense multiple targets in terrain-aware line of sight (LoS) conditions in uneven terrain equipped with directional antennas. To mitigate terrain-induced LoS blockages that degrade detection performance, we incorporate a binary LoS indicator and propose a bounding volume hierarchy (BHV)-based adaptive scheme for efficient LoS evaluation. We formulate a bi-objective problem that maximizes the probability of cooperative detection with minimal hover energy constraints governing spatial, orientational, and safety constraints. To address the problem, which is inherently non-convex, we propose a hierarchical heuristic framework that combines exploration through a genetic algorithm (GA) with per-UAV refinement via particle swarm optimization (PSO), where a penalty-based fitness evaluation guides solutions toward feasibility, bounded within constraints. The proposed methodology is an effective trade-off method of traversing through a complex search space and maintaining terrain-aware LoS connectivity and energy aware deployment. Monte Carlo simulations on real-world terrain data show that the proposed GA+PSO framework improves detection probability by 37.02% and 36.5% for 2 and 3 UAVs, respectively, while reducing average excess hover energy by 45.0% and 48.9% compared to the PSO-only baseline. Relative to the non-optimized scheme, it further achieves 59.5% and 54.2% higher detection probability with 59.8% and 65.9% lower excess hover energy, thereby showing its effectiveness with a small number of UAVs over uneven terrain.