Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Rushi Moliya, Dhaval K. Patel, Brijesh Soni, Miguel López-Benítez

TL;DR

This work proposes a hierarchical heuristic framework that combines exploration through a genetic algorithm with per-UAV refinement via particle swarm optimization (PSO) to address the multi-unmanned aerial vehicle 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.

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.

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

TL;DR

This work proposes a hierarchical heuristic framework that combines exploration through a genetic algorithm with per-UAV refinement via particle swarm optimization (PSO) to address the multi-unmanned aerial vehicle 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.

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.

Paper Structure

This paper contains 13 sections, 20 equations, 4 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. Spectrum Sensing Model
  4. Eigenvalue-Based Detection (EBD)
  5. Directional Antenna Model
  6. Cooperative Sensing Model
  7. Energy Consumption Model
  8. Problem Formulation
  9. Proposed Methodology
  10. Proposed BVH-Based Adaptive LoS Determination
  11. Proposed Optimization Framework
  12. Numerical Results
  13. Conclusion

Figures (4)

  • Figure 1: Multi-UAV cooperative sensing network over real terrain, where $M$ UAVs detect $N$ targets using eigenvalue-based detection along with the hierarchical GA+PSO optimization flow.
  • Figure 2: 3D Deployment results in multiple UAVs perceive multiple target scenarios over real SRTM-based uneven terrain: (a) $M = 3$, (b) $M = 4$, (c) $M = 5$
  • Figure 3: Comparison of sum detection probabilities with varying numbers of UAVs deployed across different schemes.
  • Figure 4: Comparison of average excess hover energy per UAV with numbers of UAVs deployed across different schemes.