University of Illinois Urbana-Champaign

Optimizing UAV Network Efficiency: Integrative Strategies for Simultaneous Energy Management and Obstacle-Aware Routing

Basiri, Salar; Tiwari, Dhananjay; Papachristos, Christos; Salapaka, Srinivasa

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basiri-et-al-2024-optimizing-uav-network-efficiency-integrative-strategies-for-simultaneous-energy-management-and (1).pdf

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https://hdl.handle.net/2142/125285

Description

Title
Optimizing UAV Network Efficiency: Integrative Strategies for Simultaneous Energy Management and Obstacle-Aware Routing
Author(s)
  • Basiri, Salar
  • Tiwari, Dhananjay
  • Papachristos, Christos
  • Salapaka, Srinivasa
Issue Date
2024-01-04
Keyword(s)
  • UAV
  • Optimization
  • Path Planning
  • Resource Allocation
  • Traffic Management
  • Combinatorial Optimization
Date of Ingest
2025-01-07T17:43:41-06:00
Abstract
This paper addresses a pivotal challenge in Unmanned Aerial Vehicle (UAV) networks crucial for sectors including delivery services, agriculture, and emergency response: optimizing UAV charging strategies for continuous operation in monitoring and inspection missions. We introduce a novel Maximum Entropy Principle (MEP) framework that employs mobile charging vehicles (MCVs), potentially Unmanned Ground Vehicles (UGVs), for in-field UAV charging. This MEP framework marks a significant advancement in the field by providing an integrated solution for multi-objective problems, including simultaneous obstacle-aware path planning and resource management under energy limitations. Distinguished from traditional heuristic approaches, our framework adeptly handles complex scenarios, significantly reducing optimization variables and facilitating robustness analysis. Through comprehensive simulations, our methodology has demonstrated significant advantages over existing algorithms in handling complex operational scenarios. Unlike other algorithms, which often struggle with large network sizes or intricate constraints, our solution consistently delivers robust performance. Empirical evidence indicates that our method achieves more than double the cost-effectiveness compared to its counterparts, coupled with multiple orders of magnitude faster operational speed. This research not only contributes to the theoretical understanding of autonomous UAV/UGV network planning but also has significant practical implications for real-time, field-deployable solutions.
Publisher
American Institute of Aeronautics and Astronautics
Type of Resource
text
Genre of Resource
conference paper
Language
eng
Sponsor(s)/Grant Number(s)
National Aeronautics and Space Administration under Grant NASA 80NSSC22M0070
Copyright and License Information
American Institute of Aeronautics and Astronautics (2024)

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