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<p>This paper introduces a novel dual-phased scheme designed to enhance the efficiency of Unmanned Aerial Vehicles through optimized scheduling and coverage path planning. The first phase, a dynamic scheduling algorithm, systematically sequences UAV launches by accounting for operator availability, UAV readiness, and real-time constraints, showcasing a marked improvement in mission timelines and resource utilization. The second phase employs a coverage path planning strategy, utilizing a cost function that carefully balances mission completion time against target priorities through the adjustment of an alpha parameter. Comprehensive simulations across diverse operational scenarios–varying by points of interest, base stations, operators, and UAV configurations–demonstrate the significant impact of the alpha parameter on mission efficiency and coverage effectiveness. Key results indicate our framework ensures sub-optimal coverage in extensive operations while optimizing target prioritization, notably enhancing overall mission performance. The adaptability and efficiency of the scheduling algorithm are particularly highlighted in complex operational scenarios, presenting considerable time optimization opportunities. This study contributes a real-time scalable, efficient solution to UAV operational challenges, offering significant advancements in UAV deployment strategies for surveillance, disaster management, and agricultural monitoring. The findings underscore the pivotal role of alpha in balancing timely mission completion and strategic target prioritization, suggesting our approach as a potential foundation in future UAV operational research and applications.</p>

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Prioritized real-time multi-objective coverage path planning scheme for energy-constrained multi-UAVs

  • 期刊论文

  • 发表日期：

    2025年7月24日

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  • Pawan Kumar,
  • Kunwar Pal,
  • Mahesh Chandra Govil

摘要

This paper introduces a novel dual-phased scheme designed to enhance the efficiency of Unmanned Aerial Vehicles through optimized scheduling and coverage path planning. The first phase, a dynamic scheduling algorithm, systematically sequences UAV launches by accounting for operator availability, UAV readiness, and real-time constraints, showcasing a marked improvement in mission timelines and resource utilization. The second phase employs a coverage path planning strategy, utilizing a cost function that carefully balances mission completion time against target priorities through the adjustment of an alpha parameter. Comprehensive simulations across diverse operational scenarios–varying by points of interest, base stations, operators, and UAV configurations–demonstrate the significant impact of the alpha parameter on mission efficiency and coverage effectiveness. Key results indicate our framework ensures sub-optimal coverage in extensive operations while optimizing target prioritization, notably enhancing overall mission performance. The adaptability and efficiency of the scheduling algorithm are particularly highlighted in complex operational scenarios, presenting considerable time optimization opportunities. This study contributes a real-time scalable, efficient solution to UAV operational challenges, offering significant advancements in UAV deployment strategies for surveillance, disaster management, and agricultural monitoring. The findings underscore the pivotal role of alpha in balancing timely mission completion and strategic target prioritization, suggesting our approach as a potential foundation in future UAV operational research and applications.