Multi-UAV Collaborative Mission PlanningMethod for Self-Organized Sensor Data Acquisition

作     者：Yang, Shijie Yuan, Jiateng Zhang, Zhipeng Chen, Zhibo Zhang, Hanchao Cui, Xiaohui 

作者机构：School of Information Science and Technology Beijing Forestry University Beijing 100083 China Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing 100083 China State Key Laboratory of Efficient Production of Forest Resources Beijing 100083 China Division of Biostatistics Department of Population Heath Grossman School of Medicine New York University New York NY 10016 United States 

出 版 物：《Computers, Materials and Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第81卷第1期

页      面：1529-1563页

核心收录：

学科分类：0810[工学-信息与通信工程] 1202[管理学-工商管理] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0709[理学-地质学] 0828[工学-农业工程] 08[工学] 0837[工学-安全科学与工程] 0813[工学-建筑学] 0824[工学-船舶与海洋工程] 0814[工学-土木工程] 0825[工学-航空宇航科学与技术] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research was funded by National Key R&D Program of China (2022YFF13 02700)  Xiong'an New Area Science and Technology Innovation Special Project of Ministry of Science and Technology of China (2023XAGG0065)  Ant Group through CCF-Ant Research Fund (CCF-AFSG RF20220214)  Outstanding Youth Team Project of CentralUniversities (QNTD202308) and Beijing Forestry University National Training Program of Innovation and Entrepreneurship for Undergraduates (202310022097).Funding Statement: This research was funded by National Key R&D Program of China (2022YFF13 02700)  Xiong\u2019an New Area Science and Technology Innovation Special Project of Ministry of Science and Technology of China (2023XAGG0065)  Ant Group through CCF-Ant Research Fund (CCF-AFSG RF20220214)  Outstanding Youth Team Project of Central Universities (QNTD202308) and Beijing Forestry University National Training Program of Innovation and Entrepreneurship for Undergraduates (202310022097) 

主　　题：Unmanned aerial vehicles (UAV) 

摘      要：In recent years, sensor technology has been widely used in the defense and control of sensitive areas in cities, or in various scenarios such as early warning of forest fires, monitoring of forest pests and diseases, and protection of endangered animals. Deploying sensors to collect data and then utilizing unmanned aerial vehicle (UAV) to collect the data stored in the sensors has replaced traditional manual data collection as the dominantmethod. The current strategies for efficient data collection in above scenarios are still imperfect, and the low quality of the collected data and the excessive energy consumed by UAV flights are still the main problems faced in data *** regards this, this paper proposes a multi-UAV mission planning method for self-organized sensor data acquisition by comprehensively utilizing the techniques of self-organized sensor clustering,multi-UAVmission area allocation, and sub-area data acquisition scheme optimization. The improved α-hop clustering method utilizes the average transmission distance to reduce the size of the collection sensors, and the K-Dimensional method is used to form a multi-UAV cooperative workspace, and then, the genetic algorithm is used to trade-off the speed with the age of information (AoI) of the collected information and the energy consumption to form the multi-UAV data collection operation scheme. The combined optimization scheme in paper improves the performance by 95.56% and 58.21%, respectively, compared to the traditional baseline model. In order to verify the excellent generalization and applicability of the proposed method in real scenarios, the simulation test is conducted by introducing the digital elevation model data of the real terrain, and the results show that the relative error values of the proposed method and the performance test of the actual flight of the UAV are within the error interval of ±10%. Then, the advantages and disadvantages of the present method with the existing mainstr