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

2024-11-05

arXiv.org

AI Summary

This work addresses persistent inter-UAV communication requirements in multi-UAV cooperative search-and-rescue and surveillance missions. We propose a novel joint optimization framework that simultaneously considers coverage path planning, energy consumption, and communication connectivity. For the first time, we formulate communication range minimization and energy efficiency as a unified multi-objective optimization problem, ensuring both full-area coverage and end-to-end communication connectivity while reducing reliance on long-range links. Our method integrates graph-theoretic modeling, explicit embedding of communication constraints, and energy-aware path planning. We evaluate it via simulations and real-world three-UAV flight tests in complex, irregular regions with no-fly zones. Simulation results demonstrate a significant reduction in required communication range. Empirical validation shows 99% accuracy in communication distance estimation—outperforming state-of-the-art approaches.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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, our approach generates coverage paths that require minimal the communication range to maintain inter-UAV connectivity while also optimizing energy consumption. The mCPP problem is formulated as a multi-objective optimization task, aiming to minimize both the communication range requirement and energy consumption. Our approach significantly reduces the communication range needed for maintaining connectivity while ensuring energy efficiency, outperforming state-of-the-art methods. Its effectiveness is validated through simulations on complex and arbitrary shaped regions of interests, including scenarios with no-fly zones. Additionally, real-world experiment demonstrate its high accuracy, achieving 99% consistency between the estimated and actual communication range required during a multi-UAV coverage mission involving three UAVs.

Problem

• Minimize communication range for inter-UAV connectivity
• Optimize energy consumption in multi-UAV coverage paths
• Handle complex regions with no-fly zones efficiently

Innovation

• Minimizes communication range for UAV connectivity
• Optimizes energy consumption in path planning
• Validated via simulations and real-world experiments