MOBILE LOGISTICS AND MONITORING SYSTEMS BASED ON UAV SWARMS: CHALLENGES AND DEVELOPMENT DIRECTIONS
DOI:
https://doi.org/10.26906/SUNZ.2024.4.096Keywords:
UAV swarms, mobile logistics systems, unmanned aerial vehicles, logistics efficiency, autonomous control systems, digitalization of logistics, big data processing, data security, standardization, fault tolerance, route optimizationAbstract
With the development of technology and the active digitalization of aspects of life from transport to trade, there is a growing need to create reliable mobile logistics systems based on swarms of unmanned aerial vehicles (UAVs). The use of UAV swarms can significantly improve the efficiency of logistics and monitoring operations, ensuring safety and optimizing costs through the collection and analysis of data for real-time decision-making. The main goal of this article is to review the methods and software tools that ensure the reliable operation of mobile logistics and monitoring systems based on UAV swarms. Key areas of activity are also considered with examples of their application, characteristic features, problems, limitations and advantages. The article also outlines the general challenges and limitations in this field. The paper proposes the concept of using UAV swarms to solve logistical and monitoring tasks that increase work efficiency and reduce the risk of unforeseen situations during the life cycle of operations. There are three main approaches to implementing such systems: autonomous UAV swarms, operator-controlled swarms, and hybrid systems. In addition, the application of UAV swarms in various fields of activity, such as the delivery of goods, monitoring of warehouses and infrastructure, as well as support of search and rescue operations, is considered. The concept of mobile logistics and monitoring systems based on UAV swarms can be implemented in almost all industries, but this article focuses on the most common areas that have significantly influenced the development of such systems. An analysis of the main fields of application of UAV swarms was carried out, the features and problems of their use in each of the considered spheres of activity were determined. The challenges, advantages and specifics of using UAV swarms were summarized.Downloads
References
Joonyup Eun, Byung Duk Song, Songbook Lee and Dae-Eun Lim, Mathematical Investigation on the Sustainability of UAV Logistics. Sustainability 2019, 11(21), 5932; https://doi.org/10.3390/su11215932
Yi Li, Min Liu and Dandan Jiang, Application of Unmanned Aerial Vehicles in Logistics: A Literature Review. Sustainability 2022, 14(21), 14473; https://doi.org/10.3390/su142114473
Hanxue Li, Shuaiqi Zhu, Amr Tolba, Ziyi Liu and Wu Wen, A Reliable Delivery Logistics System Based on the Collaboration of UAVs and Vehicles. Sustainability 2023, 15(17), 12720; https://doi.org/10.3390/su151712720
Shan Li, Honghai Zhang, Zhuolun Li and Hao Liu, An Air Route Network Planning Model of Logistics UAV Terminal Distribution in Urban Low Altitude Airspace. Sustainability 2021, 13(23), 13079; https://doi.org/10.3390/su132313079
Chommaphat Malang ,Phasit Charoenkwan, and Ratapol Wudhikarn, Implementation and Critical Factors of Unmanned Aerial https://doi.org/10.3390/drones7020080
Minyi Deng, Qingqing Yang and Yi Peng, A Real-Time Path Planning Method for Urban Low-Altitude Logistics UAVs. Sensors 2023, 23(17), 7472; https://doi.org/10.3390/s23177472
Muhammad Yeasir Arafat, Md Arafat Habib and Sangman Moh, Routing Protocols for UAV-Aided Wireless Sensor Networks. Appl. Sci. 2020, 10(12), 4077; https://doi.org/10.3390/app10124077
Saif Ullah, Khalid Hussain Mohammadani, Muhammad Asghar Khan, Zhi Ren, Reem Alkanhel, Ammar Muthanna and Usman Tariq, Position-Monitoring-Based Hybrid Routing Protocol for 3D UAV-Based Networks. Drones 2022, 6(11), 327;https://doi.org/10.3390/drones6110327
Ghulam E. Mustafa Abro, Saiful Azrin B. M. Zulkifli, Rana Javed Masood, Vijanth Sagayan Asirvadam and Anis Laouiti, Comprehensive Review of UAV Detection, Security, and Communication Advancements to Prevent Threats. Drones 2022, 6(10), 284; https://doi.org/10.3390/drones6100284
Asmaa Abdallah, M. Zulfiker Ali, Jelena Mišić and Vojislav B. Mišić, Efficient Security Scheme for Disaster Surveillance UAV Communication Networks. Information 2019, 10(2), 43; https://doi.org/10.3390/info10020043
Marlena Robakowska, Daniel Ślęzak, Przemysław Żuratyński, Anna Tyrańska-Fobke, Piotr Robakowski, Paweł Prędkiewicz and Katarzyna Zorena, Possibilities of Using UAVs in Pre-Hospital Security for Medical Emergencies. Int. J. Environ. Res. Public Health 2022, 19(17), 10754; https://doi.org/10.3390/ijerph191710754
Khalifa AL-Dosari and Noora Fetais, A New Shift in Implementing Unmanned Aerial Vehicles (UAVs) in the Safety and Security of Smart Cities: A Systematic Literature Review. Safety 2023, 9(3), 64; https://doi.org/10.3390/safety9030064
Wedad Alawad, Nadhir Ben Halima and Layla Aziz, An Unmanned Aerial Vehicle (UAV) System for Disaster and Crisis Management in Smart Cities. Electronics 2023, 12(4), 1051; https://doi.org/10.3390/electronics12041051
Nadir Abbas, Zeshan Abbas, Xiaodong Liu, Saad Saleem Khan, Eric Deale Foster and Stephen Larkin, A Survey: Future Smart Cities Based on Advance Control of Unmanned Aerial Vehicles (UAVs). Appl. Sci. 2023, 13(17), 9881; https://doi.org/10.3390/app13179881
Vyacheslav Kharchenko, Ihor Kliushnikov, Andrzej Rucinski, Herman Fesenko and Oleg Illiashenko, UAV Fleet as a Dependable Service for Smart Cities: Model-Based Assessment and Application. Smart Cities 2022, 5(3), 1151-1178; https://doi.org/10.3390/smartcities5030058
Adiel Ismail, Bigomokero Antoine Bagula and Emmanuel Tuyishimire, Internet-Of-Things in Motion: A UAV Coalition Model for Remote Sensing in Smart Cities. Sensors 2018, 18(7), 2184; https://doi.org/10.3390/s18072184
Enrico Petritoli, Fabio Leccese, and Lorenzo Ciani,Reliability and Maintenance Analysis of Unmanned Aerial Vehicles. Sensors 2018, 18(9), 3171; https://doi.org/10.3390/s18093171
Krzysztof Andrzej Gromada and Wojciech Marcin Stecz, Designing a Reliable UAV Architecture Operating in a Real Environment. Appl. Sci. 2022, 12(1), 294; https://doi.org/10.3390/app12010294
Ning Ning, Suiping Zhou, Weimin Bao and Xiaoping Li, A Study on the Maximum Reliability of Multi-UAV Cooperation Relay Systems. Sensors 2024, 24(9), 2886; https://doi.org/10.3390/s24092886
Taha Elmokadem and Andrey V. Savkin, Towards Fully Autonomous UAVs: A Survey. Sensors 2021, 21(18), 6223; https://doi.org/10.3390/s21186223
Nurul I. Sarkar, and Sonia Gul, Artificial Intelligence-Based Autonomous UAV Networks: A Survey. Drones 2023, 7(5), 322; https://doi.org/10.3390/drones7050322
Victor M. Becerra, Autonomous Control of Unmanned Aerial Vehicles. Electronics 2019, 8(4), 452; https://doi.org/10.3390/electronics8040452
Krzysztof Mateja, Wojciech Skarka, Magdalena Peciak, Roman Niestrój and Maik Gude, Energy Autonomy Simulation Model of Solar Powered UAV. Energies 2023, 16(1), 479; https://doi.org/10.3390/en16010479
Paula Fraga-Lamas, Lucía Ramos, Víctor Mondéjar-Guerra and Tiago M. Fernández-Caramés, A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance. Remote Sens. 2019, 11(18), 2144; https://doi.org/10.3390/rs11182144
Yassine Yazid, Imad Ez-Zazi, Antonio Guerrero-González, Ahmed El Oualkadi and Mounir Arioua, UAV-Enabled Mobile Edge-Computing for IoT Based on AI: A Comprehensive Review. Drones 2021, 5(4), 148; https://doi.org/10.3390/drones5040148
Anis Koubaa, Adel Ammar, Mohamed Abdelkader, Yasser Alhabashi and Lahouari Ghouti, AERO: AI-Enabled Remote Sensing Observation with Onboard Edge Computing in UAV. Remote Sens. 2023, 15(7), 1873; https://doi.org/10.3390/rs15071873
Petros S. Bithas, Emmanouel T. Michailidis, Nikolaos Nomikos, Demosthenes Vouyioukas and Athanasios G. Kanatas, A Survey on Machine-Learning Techniques for UAV-Based Communications. Sensors 2019, 19(23), 5170; https://doi.org/10.3390/s19235170
Roghieh Eskandari, Masoud Mahdianpari, Fariba Mohammadimanesh, Bahram Salehi, Brian Brisco and Saeid Homayouni, Meta-analysis of Unmanned Aerial Vehicle (UAV) Imagery for Agro-environmental Monitoring Using Machine Learning and Statistical Models. Remote Sens. 2020, 12(21), 3511; https://doi.org/10.3390/rs12213511
Vittorio Mazzia, Lorenzo Comba, Aleem Khaliq, Marcello Chiaberge and Paolo Gay, UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture.Sensors 2020, 20(9), 2530; https://doi.org/10.3390/s20092530
Chamali Sandamini, Madduma Wellalage Pasan Maduranga, Valmik Tilwari, Jamaiah Yahaya, Faizan Qamar, Quang Ngoc Learning Algorithms. Electronics 2023, 12(7), 1533; https://doi.org/10.3390/electronics12071533
Geun-Ho Kwak and No-Wook Park, Impact of Texture Information on Crop Classification with Machine Learning and UAV Images. Appl. Sci. 2019, 9(4), 643; https://doi.org/10.3390/app9040643
Hisham Khalil, Saeed Ur Rahman, Inam Ullah, Inayat Khan, Abdulaziz Jarallah Alghadhban, Mosleh Hmoud Al-Adhaileh, Gauhar Ali and Mohammed ElAffendi, A UAV-Swarm-Communication Model Using a Machine-Learning Approach for Search-and-Rescue Applications. Drones 2022, 6(12), 372; https://doi.org/10.3390/drones6120372
Daniel H. Stolfi and Grégoire Danoy, An Evolutionary Algorithm to Optimise a Distributed UAV Swarm Formation System. Appl. Sci. 2022, 12(20), 10218; https://doi.org/10.3390/app122010218
Ángel Madridano, Abdulla Al-Kaff, David Martín and Arturo de la Escalera, 3D Trajectory Planning Method for UAVs Swarm in Building Emergencies. Sensors 2020, 20(3), 642; https://doi.org/10.3390/s20030642
Abhishek Phadke and F. Antonio Medrano, Towards Resilient UAV Swarms—A Breakdown of Resiliency Requirements in UAV Swarms. Drones 2022, 6(11), 340; https://doi.org/10.3390/drones6110340
Rui Ming, Rui Jiang, Haibo Luo, Taotao Lai, Ente Guo and Zhiyan Zhou, Comparative Analysis of Different UAV Swarm Control Methods on Unmanned Farms. Agronomy 2023, 13(10), 2499; https://doi.org/10.3390/agronomy13102499
Xudong Deng, Mingke Guan, Yunfeng Ma, Xijie Yang and Ting Xiang, Vehicle-Assisted UAV Delivery Scheme Considering Energy Consumption for Instant Delivery. Sensors 2022, 22(5), 2045; https://doi.org/10.3390/s22052045
Jianxun Li, Hao Liu, Kin Keung Lai and Bhagwat Ram, Vehicle and UAV Collaborative Delivery Path Optimization Model. Mathematics 2022, 10(20), 3744; https://doi.org/10.3390/math10203744
Young Kwan Ko, Ju Hyeong Park and Young Dae Ko, A Development of Optimal Algorithm for Integrated Operation of UGVs and UAVs for Goods Delivery at Tourist Destinations. Appl. Sci. 2022, 12(20), 10396; https://doi.org/10.3390/app122010396
Fang Li and Oliver Kunze, A Comparative Review of Air Drones (UAVs) and Delivery Bots (SUGVs) for Automated Last Mile Home Delivery. Logistics 2023, 7(2), 21; https://doi.org/10.3390/logistics7020021
Emanuel Jesús Ulin Hernández, Jania Astrid Saucedo Martínez and José Antonio Marmolejo Saucedo, Optimization of the Distribution Network Using an Emerging Technology. Appl. Sci. 2020, 10(3), 857; https://doi.org/10.3390/app10030857
Diyar Altinses, David Orlando Salazar Torres, Michael Schwung, Stefan Lier and Andreas Schwung, Optimizing Drone Logistics: A Scoring Algorithm for Enhanced Decision Making across Diverse Domains in Drone Airlines. Drones 2024, 8(7), 307; https://doi.org/10.3390/drones8070307
DroneARchery: Human-Drone Interaction through Augmented Reality with Haptic Feedback and Multi-UAV Collision Avoidance Driven by Deep Reinforcement Learning, available at: https://ar5iv.labs.arxiv.org/html/2210.07730 (accessed August, 2024).
Ihor Kliushnikov, Vyacheslav Kharchenko, Herman Fesenko, Multi-UAV Routing for Critical Iinfrastructure Monitoring Considering Failures of UAVs: Reliability Models, Rerouting Algorithms, Industrial Case, available at: https://www.researchgate.net/profile/Elena-Zaitseva-2/publication/353590931_MultiUAV_Routing_for_Critical_Iinfrastructure_Monitoring_Considering_Failures_of_UAVs_Reliability_Models_Rerouting_Al gorithms_Industrial_Case/links/61124f10169a1a0103ee1d92/Multi-UAV-Routing-for-Critical-Iinfrastructure-MonitoringConsidering-Failures-of-UAVs-Reliability-Models-Rerouting-Algorithms-Industrial-Case.pdf (accessed August, 2024).
Herman Fesenko, Oleg Illiashenko, Vyacheslav Kharchenko, Ihor Kliushnikov, Olga Morozova, Anatoliy Sachenko and Stanislav Skorobohatko, Flying Sensor and Edge Network-Based Advanced Air Mobility Systems: Reliability Analysis and Applications for Urban Monitoring. Drones 2023, 7(7), 409; https://doi.org/10.3390/drones7070409
Yun Sun, Herman Fesenko, Vyacheslav Kharchenko, Luo Zhong, Ihor Kliushnikov, Oleg Illiashenko, Olga Morozova and Anatoliy Sachenko, UAV and IoT-Based Systems for the Monitoring of Industrial Facilities Using Digital Twins: Methodology, Reliability Models, and Application. Sensors 2022, 22(17), 6444; https://doi.org/10.3390/s22176444
