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Communication and Energy-Aware Multi-UAV Coverage Path Planning for Networked Operations

Mohamed Samshad, Ketan Rajawat

TL;DR

The paper tackles multi-UAV coverage path planning under inter-UAV communication constraints by integrating a lightweight energy model with a connectivity-radius estimator to minimize $f_o(\mathcal{T}) = r(\mathcal{T}) + \lambda e(\mathcal{T})$. It introduces an end-to-end planner that couples DARP for area division with STC for path generation and employs a nested Bayesian optimization (TPE) to search over seed points $\mathbf{p}$ and launch points $\mathbf{k}$, enabling near-global optimization in irregular, obstacle-rich ROIs. A Lipschitz property of the connectivity radius and efficient MBST-based estimation accelerate the connectivity assessment, while a formation option supports tight-connectivity regimes. Evaluations in simulation and real-world tests with three UAVs demonstrate substantial reductions in connectivity requirements (20–60%) and high accuracy in range estimation (≈99.9%), highlighting the method's practical value for SAR, surveillance, and other cooperative missions that rely on continuous inter-UAV communication.

Abstract

This paper presents a communication and energy-aware multi-UAV Coverage Path Planning (mCPP) method for scenarios requiring continuous inter-UAV communication, such as cooperative search and rescue and surveillance missions. Unlike existing mCPP solutions that focus on energy, time, or coverage efficiency, the proposed method generates coverage paths that minimize a specified combination of energy and inter-UAV connectivity radius. Key features of the proposed algorithm include a simplified and validated energy consumption model, an efficient connectivity radius estimator, and an optimization framework that enables us to search for the optimal paths over irregular and obstacle-rich regions. The effectiveness and utility of the proposed algorithm is validated through simulations on various test regions with and without no-fly-zones. Real-world experiments on a three-UAV system demonstrate the remarkably high 99% match between the estimated and actual communication range requirement.

Communication and Energy-Aware Multi-UAV Coverage Path Planning for Networked Operations

TL;DR

The paper tackles multi-UAV coverage path planning under inter-UAV communication constraints by integrating a lightweight energy model with a connectivity-radius estimator to minimize . It introduces an end-to-end planner that couples DARP for area division with STC for path generation and employs a nested Bayesian optimization (TPE) to search over seed points and launch points , enabling near-global optimization in irregular, obstacle-rich ROIs. A Lipschitz property of the connectivity radius and efficient MBST-based estimation accelerate the connectivity assessment, while a formation option supports tight-connectivity regimes. Evaluations in simulation and real-world tests with three UAVs demonstrate substantial reductions in connectivity requirements (20–60%) and high accuracy in range estimation (≈99.9%), highlighting the method's practical value for SAR, surveillance, and other cooperative missions that rely on continuous inter-UAV communication.

Abstract

This paper presents a communication and energy-aware multi-UAV Coverage Path Planning (mCPP) method for scenarios requiring continuous inter-UAV communication, such as cooperative search and rescue and surveillance missions. Unlike existing mCPP solutions that focus on energy, time, or coverage efficiency, the proposed method generates coverage paths that minimize a specified combination of energy and inter-UAV connectivity radius. Key features of the proposed algorithm include a simplified and validated energy consumption model, an efficient connectivity radius estimator, and an optimization framework that enables us to search for the optimal paths over irregular and obstacle-rich regions. The effectiveness and utility of the proposed algorithm is validated through simulations on various test regions with and without no-fly-zones. Real-world experiments on a three-UAV system demonstrate the remarkably high 99% match between the estimated and actual communication range requirement.

Paper Structure

This paper contains 15 sections, 1 theorem, 11 equations, 5 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. RELATED WORK
  3. PROBLEM FORMULATION
  4. Estimation of total energy e(T)
  5. Estimation of connectivity radius r(T)
  6. Proposed Approach
  7. Area Division, and Path Planning
  8. Area Division
  9. Path Planning
  10. Optimal Trajectories
  11. Evaluation results
  12. Hardware Specifications and Flight Metrics
  13. Validation in Simulation
  14. Real World Experiments
  15. CONCLUSION

Key Result

Theorem 1

The communication radius $r(t)$ is $2v$-Lipschitz continuous, i.e., $|r(t) - r(t')| \leq 2v |t - t'|$ for any $t, t' \in [0,T]$.

Figures (5)

  • Figure 1: Optimization process breakdown: ROI Discretization through optimal shifts and rotation, DARP-MST path generation, time-sampling & energy estimation, launch point optimization by connectivity analysis, Bayesian optimization with range and energy for next DARP initialization
  • Figure 2: Performance comparison of the proposed algorithm versus state-of-the-art methods across various ROIs
  • Figure 3: Scenarios used for experimental evaluation of the proposed method. Shade-of-grey polygons represent arbitrary ROI, blue polygon is the processed ROI and solid red polygons are NFZs.
  • Figure 4: Connectivity strength along UAV paths
  • Figure 5: Range requirement vs time

Theorems & Definitions (1)

  • Theorem 1