Multi-dimensional Resource Joint Optimization Algorithm for UAV Inspection of Collaborative Tasks of Perception and AI

LI Shiyang; ZHU Xiaorong

doi:10.11999/JEIT251284

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LI Shiyang, ZHU Xiaorong. Multi-dimensional Resource Joint Optimization Algorithm for UAV Inspection of Collaborative Tasks of Perception and AI[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251284

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LI Shiyang, ZHU Xiaorong. Multi-dimensional Resource Joint Optimization Algorithm for UAV Inspection of Collaborative Tasks of Perception and AI[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251284

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Multi-dimensional Resource Joint Optimization Algorithm for UAV Inspection of Collaborative Tasks of Perception and AI

doi: 10.11999/JEIT251284 cstr: 32379.14.JEIT251284

LI Shiyang,
ZHU Xiaorong^,

School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjin 210003, China

Funds: The National Natural Science Foundation of China (92367102), The Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX22_0944)

Received Date: 2025-12-03
Accepted Date: 2026-03-03
Rev Recd Date: 2026-02-25

Available Online: 2026-03-15

Abstract

Abstract

Objective With increasing demand for aerial operations, the capabilities of various aircraft are steadily expanding across all airspace levels and multiple industries. The application of Unmanned Aerial Vehicles (UAVs) now spans multiple altitude layers, from low to high altitudes, and covers micro, medium, and large models. UAVs are widely used in public safety, transportation, emergency management, logistics and distribution, geographic surveying and mapping, and other fields, thereby promoting innovation and transformation in production and daily life. Compared with traditional manual inspection, UAV inspection, as an emerging operational approach, can acquire image information that is difficult for the human eye to capture. Labor costs are therefore significantly reduced, and the accuracy and efficiency of inspection operations are improved. However, UAV inspection also creates new challenges for multidimensional resource allocation and task scheduling. In power system inspection, for example, transmission lines are exposed to outdoor environments for long periods and are vulnerable to corrosion, aging, and even damage. Regular inspections are therefore required to ensure operational safety. Methods A four-stage multidimensional resource inspection and scheduling collaborative optimization algorithm is proposed. The original optimization problem is decomposed into four subproblems according to the inspection process. After mathematical analysis of each subproblem, a corresponding solution method is proposed. For the node selection problem, a dual-aided Mixed-Integer Linear Programming (MILP) transformation method is used. For the UAV data acquisition problem, a data-driven boundary learning method is adopted. For UAV communication resource allocation, a bandwidth-power joint optimization algorithm based on Successive Convex Approximation (SCA) is used. For node computing power allocation, a lower-bound analytical allocation method is adopted. Finally, the original problem is solved by an alternating optimization method across the subproblems, thereby forming the complete algorithm. Results and Discussions Simulation results show that the proposed algorithm reduces overall UAV energy consumption compared with the benchmark algorithms. Simulation training is conducted for visual positioning and fault detection services to examine the relationship among compression ratio, data volume, and service performance. Figures 2-5 show that fault detection accuracy reaches its optimum at 60% data volume and 60% compression ratio. Visual positioning accuracy reaches its optimum at 80% data volume and 80% compression ratio. Figure 6 shows that the proposed algorithm achieves higher accuracy than the benchmark algorithms for AI services. As shown in Figures 7 and 8, under varying bandwidth, computing power, and other resource conditions, the proposed algorithm consistently performs better than the benchmark algorithms in terms of energy consumption and effectively reduces total energy consumption. Conclusions A multidimensional resource joint optimization algorithm is proposed for intelligent UAV inspection with collaborative perception and AI tasks. An optimization problem is formulated with the objective of minimizing UAV energy consumption, using bandwidth, power, computing power, node selection, data volume, and actual compression ratio as variables. The algorithm jointly minimizes UAV energy consumption for two AI services, fault detection and visual localization. Simulation results show that the algorithm reduces total UAV energy consumption and improves model training accuracy. This study focuses on the application scenario of single-UAV inspection. More complex multi-UAV collaborative inspection scenarios can be examined in future work, and additional services can be incorporated for a more comprehensive analysis.
- Unmanned Aerial Vehicle(UAV),
- Intelligent inspection,
- Resource scheduling,
- Joint optimization

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References(22)

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