A Review of Drone Delivery Models and Key Technologies
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摘要: 无人机配送作为1种新兴的运输模式，近年来受到广泛关注。然而，无人机续航能力有限、载重量不足以及复杂环境适应性不强等技术瓶颈，制约着其大规模应用。针对上述问题，梳理了无人机配送模式及关键技术的研究进展，为进一步提升无人机配送效率和应用范围提供参考。在配送模式方面，无人机配送模式呈现多样化趋势，包括独立配送模式、无人机与车辆协同配送等多种模式；在多种配送模式对比的基础上，归纳了各模式的优化模型、适用场景与挑战及配送性能。在关键技术方面，从无人机性能提升到运输模式风险分析，研究人员开展了大量工作：①面向无人机长航程及高效补能需求，以无人机为对象开展了多能源融合、轻量化设计、载荷设计与优化、电池优化等方法研究，以换电站为对象开展了固定式与移动式充电站、自动换电站等技术研究；②为实现高精度定位与姿态估计，惯性导航系统、视觉里程计、即时定位及建图（simultaneous localization and mapping，SLAM）、多传感器融合等技术的融合成为趋势，并通过与深度学习、协同优化等算法，实现无人机自主导航、避障及多无人机协同效率的提升；③为增强无人机通信的实时性与安全性，5G通信、边缘计算、分布式架构、区块链等新兴技术成为热点；④在无人机配送的风险评估、故障处理、环境适应性提升及动态监控中，无人机行为识别、异常检测等技术大量使用。未来，面向配送无人机性能提升需求，应开展多源融合导航与智能协同避障研究，构建空天地一体化的通信网络与智能主动防御体系，增强配送无人机鲁棒性、环境适应性与实时监控能力；面向无人机配送运营需求，应开展多无人机配送模式协同与动态切换机制研究，提出多目标动态优化配送模型，突破无人机新能源应用瓶颈，发展充换电融合技术。Abstract: As an emerging logistics mode, drone delivery has garnered significant attention in recent years. However, technical bottlenecks including limited endurance, insufficient payload capacity, and poor adaptability to complex environments constrain its large-scale implementation. Addressing these challenges, this paper reviews advances in delivery modes of the drone and key technologies to enhance its operational efficiency and application scope. In terms of delivery modes, diversification trends are observed, encompassing drone-only delivery, drone-vehicle collaborative delivery, and others. Comparative analysis reveals optimization models, applicable scenarios, challenges, and performance metrics of different drone delivery modes. Key technological advances include: ①Endurance solutions through multi-energy systems, lightweight design, payload optimization, and charging infrastructure. ②Enhanced navigation precision via fused inertial navigation systems (INS), visual odometry, simultaneous localization and mapping (SLAM), and other multi-sensor systems augmented by deep learning for autonomous obstacle avoidance. ③Secure communications leveraging 5G, edge computing, and blockchain. ④Risk mitigation using behavior recognition and anomaly detection. In the future, the research should prioritize multi-source navigation, intelligent obstacle avoidance, space-air-ground networks, and proactive defense systems to strengthen robustness and monitoring of delivery drones; additionally, dynamic mode switching designs, multi-objective optimization models, and integrated charging-swapping technologies for overcoming energy constraints require further investigation.
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图 10 深度学习与计算机视觉多传感器融合避障及路径规划实现流程图
Figure 10. Flowchart of deep learning and computer vision-based multi-sensor fusion for obstacle avoidance and path planning implementation
表 1 无人机与车辆同步配送分类
Table 1. The classification of collaborative distribution problems of Drones and Vehicles
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表 2 无人机各配送模式适用场景与挑战
Table 2. Applicable scenarios and challenges of drone delivery modes
配送模式 适用场景 挑战与限制 无人机独立配送 短途、小规模配送、客户点距离配送中心较近 高运营成本、续航能力不足、对天气敏感、载重和覆盖范围限制、严格法规要求 无人机与车辆同步配送 城市高峰期、多配送点区域、农村、应急物流等场景 调度复杂、依赖车辆位置、应对突发需求的灵活性不足 无人机与车辆并行配送 城市拥堵、高层建筑、偏远地区、山区、紧急医疗运输等场景 高成本、精确协调要求高、操作难度大、对基础设施支持需求高 车辆支持无人机配送 城市末端配送、应急物流、山区特产配送等场景 对车辆高度依赖、灵活性不足、改装和维护成本较高 无人机支持车辆配送 城市快递、应急医疗物资递送、灾害响应、远程维修服务等场景 对无人机操作和调度技术要求高 混合配送 灵活性较高，适用于不同应用场景 运营管理复杂、协调和同步难度大、技术集成兼容性和稳定性问题
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表 3 配送性能对比
Table 3. The comparison of delivery performance
配送模式 配送速度 可配送规模 配送成本 适用场景 无人机独立配送 快 小 高 少 无人机与车辆同步配送 中 中 中 中 无人机与车辆并行配送 中 中 中 中 车辆支持无人机配送 快 中 中 中 无人机支持车辆配送 慢 中 中 中 混合配送 中 大 中 多
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表 6 无人机配送关键技术分析
Table 6. Analysis of key technologies for uav-based delivery
技术类别 核心技术 对无人机配送的技术支持 对多无人机协同配送的技术支持 对无人机与车辆协同配送的技术支持 无人机续航与充换电技术 续航、充换电技术 延长续航时间，提升能源利用效率，保障长距离及持续配送 提升多无人机协同作业的续航能力，支持长时间任务执行 车辆为无人机提供充电和电池更换服务，提升远程配送的持续性 无人机飞行控制与导航技术 导航、避障与路径规划、协同调度技术 提供高精度定位、路径优化及动态避障能力 实现多机协同路径规划、动态调整及航路冲突规避 实现无人机与车辆的动态跟踪、路径协调及精准交付 无人机通信与网络技术 无线通信、数据链路、网络架构、传感器融合、网络安全和自适应技术 实现实时数据交互、状态监控和路径优化，提高配送效率和稳定性 支持多机实时通信、任务分配及数据共享，保障协同调度 实现无人机与地面车辆之间的实时数据交互和任务协作 无人机安全与监控技术 风险评估、应急响应与故障处理、环境适应性、监控与识别技术 提高无人机在复杂环境中的安全性，降低配送风险 提供实时状态监控、故障处理与风险评估，提升协同作业安全性 优化协同作业环境的风险管理及异常状态快速响应
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[1] SCHMIDT S, SARACENI A. Consumer acceptance of drone-based technology for last mile delivery[J]. Research in Transportation Economics, 2024, 103: 101414. doi: 10.1016/j.retrec.2024.101414 [2] 任璇, 黄辉, 于少伟, 等. 车辆与无人机组合配送研究综述[J]. 控制与决策, 2021, 36(10): 2313-2327.REN X, HUANG H, YU S W, et al. Review on vehicle-UAV combined delivery problem[J]. Control and Decision, 2021, 36(10): 2313-2327. (in Chinese) [3] MADANI B, NDIAYE M. Hybrid truck-drone delivery systems: a systematic literature review[J]. Access, 2022(10): 92854-92878. [4] CHU Y, HO C, LEE Y, et al. Development of a solar-powered unmanned aerial vehicle for extended flight endurance[J]. Drones, 2021, 5(2): 44. doi: 10.3390/drones5020044 [5] CHODNICKI M, SIEMIATKOWSKA B, STECZ W, et al. Energy efficient UAV flight control method in an environment with obstacles and gusts of wind[J]. Energies, 2022, 15 (10): 3730. doi: 10.3390/en15103730 [6] 朱贺, 黄辰雷, 杨利明, 等. 基于响应面法和拓扑优化的四旋翼无人机机架结构优化研究[J]. 机械设计, 2023, 40(增刊2): 130-135.ZHU H, HUANG C L, YANG L M, et al. Optimization of quadrotor UAV frame structure based on response surface method and topology optimization[J]. Journal of Machine Design, 2023, 40(S2): 130-135. (in Chinese) [7] 邓舒豪, 雷涛, 金贤球, 等. 燃料电池无人机混合电源系统稳定性及功率控制方法[J]. 航空学报, 2024, 45(17): 146-162.DENG S H, LEI T, JIN X Q, et al. Stability and power control method of hybrid power system for fuel cell UAVs[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(17): 146-162. (in Chinese) [8] 张启钱, 许卫卫, 张洪海, 等. 复杂低空物流无人机路径规划[J]. 北京航空航天大学学报, 2020, 46(7): 1275-1286.ZHANG Q Q, XU W W, ZHANG H H, et al. Path planning for logistics UAV in complex low-altitude airspace[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(7): 1275-1286. (in chinese) [9] DENG M, YANG Q, PENG Y. A real-time path planning method for urban low-altitude logistics UAVs[J]. Sensors, 2023, 23(17): 7472. doi: 10.3390/s23177472 [10] SHEN K, SHIVGAN R, MEDINA J, et al. Multidepot drone path planning with collision avoidance[J]. Internet of Things Journal, 2022, 9(17): 16297-16307. doi: 10.1109/JIOT.2022.3151791 [11] SCHERMER D, MOEINI M, WENDT O. A branch-and-cut approach and alternative formulations for the traveling salesman problem with drone[J]. Networks, 2020, 76(2): 164-186. doi: 10.1002/net.21958 [12] TINIÇ G O, KARASAN O E, KARA B Y, et al. Exact solution approaches for the minimum total cost traveling salesman problem with multiple drones[J]. Transportation Research Part B: Methodological, 2023, 168: 81-123. doi: 10.1016/j.trb.2022.12.007 [13] 宋瑞, 边疆, 何世伟, 等. 考虑超重超远客户的卡车无人机协同配送研究[J]. 中国公路学报, 2024, 37(3): 395-406.SONG R, BIAN J, HE S W, et al. Truck-drone joint delivery with consideration given to customers with great demands and at great distances[J]. China Journal of Highway and Transport, 2024, 37(3): 395-406. (in Chinese) [14] WU G, MAO N, LUO Q, et al. Collaborative truck-drone routing for contactless parcel delivery during the epidemic[J]. Transactions on Intelligent Transportation Systems, 2022, 23(12): 25077-25091. doi: 10.1109/TITS.2022.3181282 [15] LIN M, CHEN Y, HAN R, et al. Discrete optimization on truck-drone collaborative transportation system for delivering medical resources[J]. Discrete Dynamics in Nature and Society, 2022(1): 1811288. [16] KIM S, MOON I. Traveling salesman problem with a drone station[J]. Transactions on Systems, Man, Cybernetics: Systems, 2018, 49(1): 42-52. [17] WU G, FAN M, SHI J, et al. Reinforcement learning based truck-and-drone coordinated delivery[J]. Transactions on Artificial Intelligence, 2021, 4(4): 754-763. [18] BI Z, GUO X, WANG J, et al. Deep reinforcement learning for truck-drone delivery problem[J]. Drones, 2023, 7(7): 445. doi: 10.3390/drones7070445 [19] CHOUDHURY S, SOLOVEY K, KOCHENDERFER M J, et al. Efficient large-scale multi-drone delivery using transit networks[J]. Journal of Artificial Intelligence Research, 2021, 70: 757-788. doi: 10.1613/jair.1.12450 [20] DAYARIAN I, SAVELSBERGH M, CLARKE J-P. Same-day delivery with drone resupply[J]. Transportation Science, 2020, 54(1): 229-249. doi: 10.1287/trsc.2019.0944 [21] WANG D, HU P, DU J, et al. Routing and scheduling for hybrid truck-drone collaborative parcel delivery with independent and truck-carried drones[J]. Internet of Things Journal, 2019(6): 10483-10495. [22] TEIMOURYE, RASHID R. The sustainable hybridtruck-drone delivery model with stochastic customer existence[J]. Research in Transportation Economics, 2023, 100: 101325. doi: 10.1016/j.retrec.2023.101325 [23] 刘正元, 王清华. 无人机和车辆协同配送映射模式综述与展望[J]. 系统工程与电子技术, 2023, 45(3): 785-796.LIU Z Y, WANG Q H. Review and prospect under the mapping mode of coordinated delivery of drones and vehicles[J]. Systems Engineering and Electronics, 2023, 45(3): 785-796. (in Chinese) [24] 崔明, 冯建民, 米征, 等. 大型无人机主结构耐久性试验加载技术[J]. 航空学报, 2022, 43(6): 397-406.CUI M, FENG J M, MI Z, et al. Loading technology for main structure of large UAV durability test[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(6): 397-406. (in Chinese) [25] MA Y, CHIANG S W, CHU X, et al. Thermal design and optimization of lithium ion batteries for unmanned aerial vehicles[J]. Energy Storage, 2019(1): e48. [26] BACANLI S S, ELGELDAWI E, TURGUT B, et al. UAV charging station placement in opportunistic networks[J]. Drones, 2022, 6(10): 293. doi: 10.3390/drones6100293 [27] CHITTOOR P K, CHOKKALINGAM B. Wireless electrification system for photovoltaic powered autonomous drone charging[J]. Transactions on Transportation Electrification, 2023, 10(2): 3002-3011. [28] 蒋金橙, 王佩月, 冯天旭, 等. 基于准双向三态协同调度的无人车和无人机逐级式无线充电应用[J]. 电工技术学报, 2024, 39(22): 6955-6979.JIANG J C, WANG P Y, FENG T X, et al. AGV and UAV stepwise wireless charging application based on quasi bidirectional three-state collaborative progressive method[J]. Transactions of China Electrotechnical Society, 2024, 39 (22): 6955-6979. (in Chinese) [29] ELSAYED M, FODA A, MOHAMED M. Autonomous drone charging station planning through solar energy harnessing for zero-emission operations[J]. Sustainable Cities Society, 2022, 86: 104122. doi: 10.1016/j.scs.2022.104122 [30] ASADI A, NURRE PINKLEY S. A monotone approximate dynamic programming approach for the stochastic scheduling, allocation, and inventory replenishment problem: applications to drone and electric vehicle battery swap stations[J]. Transportation science, 2022, 56(4): 1085-1110. doi: 10.1287/trsc.2021.1108 [31] CHEN K, ZHANG Z. In-flight wireless charging: A promising application-oriented charging technique for drones[J]. IEEE Industrial Electronics Magazine, 2023, 18(1): 6-16. [32] AL-KAFF A, ARMINGOL J M, DE LA ESCALERA A. A vision-based navigation system for unmanned aerial vehicles (UAVs)[J]. Integrated Computer-Aided Engineering, 2019, 26(3): 297-310. doi: 10.3233/ICA-190601 [33] CONTE G, DOHERTY P. Vision-based unmanned aerial vehicle navigation using geo-referenced information[J]. EURASIP Journal on Advances in Signal Processing, 2009, (24): 387308. [34] ALMAHAMID F, GROLINGER K. Autonomous unmanned aerial vehicle navigation using reinforcement learning: A systematic review[J]. Engineering Applications of Artificial Intelligence, 2022, 115: 105321. doi: 10.1016/j.engappai.2022.105321 [35] XU C. UAV patrol path planning based on machine vision and multi-sensor fusion[J]. Open Computer Science, 2023, 13 (1): 20220276. doi: 10.1515/comp-2022-0276 [36] DU Z, FENG X, LI F, et al. A lightweight UAV visual obstacle avoidance algorithm based on improved YOLOv8[J]. Computers, Materials & Continua, 2024, 81(2): 2607-2627. [37] 陈佳, 张珂, 杜英森, 等. 基于改进势场法的多无人机避碰控制方法[J]. 探测与控制学报, 2024, 46(4): 93-100.CHEN J, ZHANG K, DU Y S, et al. Improved potential field method for multi-UAV collision avoidance control[J]. Journal of Detection & Control, 2024, 46(4): 93-100. (in chinese) [38] WANG D, LI W, LIU X, et al. UAV environmental perception and autonomous obstacle avoidance: A deep learning and depth camera combined solution[J]. Computers Electronics in Agriculture, 2020, 175: 105523. doi: 10.1016/j.compag.2020.105523 [39] AWADA U, ZHANG J, CHEN S, et al. Edgedrones: co-scheduling of drones for multi-location aerial computing missions[J]. Journal of Network and Computer Applications, 2023, 215: 103632. doi: 10.1016/j.jnca.2023.103632 [40] 方城亮, 杨飞生, 潘泉. 基于MASAC强化学习算法的多无人机协同路径规划[J]. 中国科学: 信息科学, 2024, 54(8): 1871-1883.FANG C L, YANG F S, PAN Q. Multi-UAV collaborative path planning based on multi-agent soft actor critic[J]. Scientia Sinica(Informationis), 2024, 54(8): 1871-1883. (in Chinese) [41] GUPTA R, KUMARI A, TANWAR S. Fusion of blockchain and artificial intelligence for secure drone networking underlying 5G communications[J]. Transactions on Emerging Telecommunications Technologies, 2021, 32(1): 4176. doi: 10.1002/ett.4176 [42] YANMAZ E, YAHYANEJAD S, RINNER B, et al. Drone networks: Communications, coordination, and sensing[J]. Ad Hoc Networks, 2018, 68: 1-15. doi: 10.1016/j.adhoc.2017.09.001 [43] CHANG Z, GUO W, GUO X, et al. Blockchain-empowered drone networks: architecture, features, and future[J]. Network, 2021, 35(1): 86-93. [44] HE D, CHAN S, GUIZANI M. Communication security of unmanned aerial vehicles[J]. Wireless Communications, 2016, 24(4): 134-139. [45] PLIOUTSIAS A, KARANIKAS N, CHATZIMIHAILIDOU M M. Hazard analysis and safety requirements for small drone operations: to what extent do popular drones embed safety?[J]. Risk Analysis, 2018, 38(3): 562-584. doi: 10.1111/risa.12867 [46] KOCSIS SZÜRKE S, PERNESS N, FÖLDESI P, et al. A risk assessment technique for energy-efficient drones to support pilots and ensure safe flying[J]. Infrastructures, 2023, 8 (4): 67. doi: 10.3390/infrastructures8040067 [47] 张健, 王守源, 赵嶷飞, 等. 城市无人机航线飞行间隔与调控频率综合研究[J]. 交通信息与安全, 2024, 42(1): 11-18. doi: 10.3963/j.jssn.1674-4861.2024.01.002ZHANG J, WANG S Y, ZHAO Y F, et al. Comprehensive study on route flight separation and control frequency of urban UAV[J]. Journal of Transport Information and Safety, 2024, 42(1): 11-18. (in chinese) doi: 10.3963/j.jssn.1674-4861.2024.01.002 [48] YANG L, JIA G, ZHENG K, et al. An unmanned aerial vehicle troubleshooting mode selection method based on SIF-SVM with fault phenomena text record[J]. Aerospace, 2021, 8(11): 347. doi: 10.3390/aerospace8110347 [49] 蒙文跃, 杨延平, 温阳, 等. 1种临近空间太阳能无人机自主故障诊断及应急处理策略[J]. 航天控制, 2020, 38(2): 56-61.MENG W Y, YANG Y P, WEN Y, et al. An autonomous fault diagnosis and emergency rreatment srategy for solar-powered UAVs in near space[J]. Aerospace Control, 2020, 38(2): 56-61. (in Chinese) [50] ROSEMAN C A, ARGROW B M. Weather hazard risk quantification for sUAS safety risk management[J]. Journal of Atmospheric Oceanic Technology, 2020, 37(7): 1251-1268. doi: 10.1175/JTECH-D-20-0009.1 [51] GAO M, HUGENHOLTZ C H, FOX T A, et al. Weather constraints on global drone flyability[J]. Scientific Reports, 2021, 11(1): 12092. doi: 10.1038/s41598-021-91325-w [52] MISHRA B, GARG D, NARANG P, et al. Drone-surveillance for search and rescue in natural disaster[J]. Computer Communications, 2020, 156: 1-10. doi: 10.1016/j.comcom.2020.03.012 [53] KIM B, MIN H, HEO J, et al. Dynamic computation offloading scheme for drone-based surveillance systems[J]. Sensors, 2018, 18(9): 2982. doi: 10.3390/s18092982 -
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