UAVs Flight Planning Method for Multi-Scan Corridor Coverage in Remote Sensing Areas

In order to address the issue of achieving comprehensive coverage in corridor scanning path planning for unmanned aerial vehicles (UAVs) in multi-area mapping, a Dual-Sequence Multi-Scan Corridor Planning Method (DSMCPM) is proposed. In the initial stage of the process, the scanning corridors that encompass multiple areas are identified as essential flight segments. A dual-sequence encoding is applied based on the execution order of the UAV and the entry and exit orientations, thus decoupling the overall flight mission planning problem into several sequential and orientation-related issues for the scanning corridors. Subsequently, the initial dual-sequence population is generated in a uniform manner using DeCantor Expansion and binary mapping. The population size and iteration limits are set adaptively according to the scale of the tasks. By employing crossover and mutation strategies, numerous current optimal solutions are identified, with the objective of minimising the flight distance. In experimental simulations across four designed scenarios, the proposed method significantly reduces redundant flight paths compared to several existing optimization algorithms. Furthermore, it shows measurable improvements in reducing overall flight distances.

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Published
2024-10-26
DOI
https://doi.org/10.1109/cisp-bmei64163.2024.10906124
Citations
1
Primary Topic
Robotic Path Planning Algorithms
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article
Field-Weighted Citation Impact
0.25
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UAVs Flight Planning Method for Multi-Scan Corridor Coverage in Remote Sensing Areas

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article

UAVs Flight Planning Method for Multi-Scan Corridor Coverage in Remote Sensing Areas

Yao Huang, Kangsheng Dong, Xianwei Jiang, Chunming Yang, Lingfeng Liu, Mingzhi Wei
article en
1 citations

Abstract

In order to address the issue of achieving comprehensive coverage in corridor scanning path planning for unmanned aerial vehicles (UAVs) in multi-area mapping, a Dual-Sequence Multi-Scan Corridor Planning Method (DSMCPM) is proposed. In the initial stage of the process, the scanning corridors that encompass multiple areas are identified as essential flight segments. A dual-sequence encoding is applied based on the execution order of the UAV and the entry and exit orientations, thus decoupling the overall flight mission planning problem into several sequential and orientation-related issues for the scanning corridors. Subsequently, the initial dual-sequence population is generated in a uniform manner using DeCantor Expansion and binary mapping. The population size and iteration limits are set adaptively according to the scale of the tasks. By employing crossover and mutation strategies, numerous current optimal solutions are identified, with the objective of minimising the flight distance. In experimental simulations across four designed scenarios, the proposed method significantly reduces redundant flight paths compared to several existing optimization algorithms. Furthermore, it shows measurable improvements in reducing overall flight distances.

Southwest University of Science and Technology (CN), China Aerodynamics Research and Development Center (CN), State Key Laboratory of Aerodynamics
Openalex Percentile: Top 42%
Robotic Path Planning Algorithms
0.25
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