{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T05:17:13Z","timestamp":1788499033326,"version":"build-2803163510"},"reference-count":48,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Rapid, reliable, and energy-efficient data collection is essential for disaster response, where terrestrial communication networks may be disrupted or unavailable. Unmanned Aerial Vehicles (UAVs) provide a flexible means of collecting critical sensing data, but their operation is constrained by limited onboard energy, stochastic wireless conditions, complex three-dimensional environments, and stringent latency requirements. This paper presents a structured multi-UAV framework that separates mission optimisation into spatial, temporal, and safety layers. In the spatial layer, a three-dimensional Travelling Salesman Problem with Neighbourhoods (3D-TSPN) formulation enables UAVs to collect data by entering valid sensing regions rather than visiting exact sensor coordinates. An Age of Information (AoI)-aware Genetic Algorithm (GA) optimises the sensor-visitation sequence, while Rapidly Exploring Random Tree Connect (RRT-Connect) generates obstacle-aware feasible paths in the three-dimensional environment. In the temporal layer, a Lyapunov-based controller selects between local processing and binary offloading to a single Mobile Edge Computing (MEC) node according to queue backlog, processing delay, energy consumption, information freshness, wireless-link feasibility, and task deadlines. In the safety layer, continuous-time conflict detection and bounded temporal or spatial adjustments are used to monitor and mitigate inter-UAV and obstacle-related risks. The framework is evaluated under stochastic wireless, mobility, computation, and obstacle conditions using 20 independent random seeds. Across the corresponding 20 proposed-policy runs, it achieves a 100% mission-validity rate, complete sensor coverage, no dropped tasks, and zero final collision or near-miss events. Compared with planning-oriented and MEC-oriented baselines, the proposed framework achieves lower information age, average delay, processing delay, energy consumption, and system cost under the evaluated conditions, while maintaining reliable multi-UAV coordination. The layered design also clarifies the contribution of each component: 3D-TSPN provides spatial flexibility, the AoI-aware GA improves route sequencing, RRT-Connect supports obstacle-aware path feasibility, Lyapunov control enables queue-aware processing decisions, and safety monitoring supports coordinated multi-UAV operation. These results indicate that integrating spatial planning, computation control, and safety coordination within a clearly separated layered architecture can provide an effective solution for multi-UAV disaster-response data collection in complex three-dimensional environments.<\/jats:p>","DOI":"10.3390\/s26175544","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T03:19:26Z","timestamp":1788232766000},"page":"5544","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrated Trajectory Planning, MEC Offloading, and Safety Coordination for Multi-UAV Disaster Response"],"prefix":"10.3390","volume":"26","author":[{"given":"Rakan","family":"Armoush","sequence":"first","affiliation":[{"name":"School of Computing and Engineering, University of West London, London W5 5RF, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0383-3553","authenticated-orcid":false,"given":"Shidrokh","family":"Goudarzi","sequence":"additional","affiliation":[{"name":"School of Computing and Engineering, University of West London, London W5 5RF, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9736-9303","authenticated-orcid":false,"given":"Muhammad Nadeem","family":"Khan","sequence":"additional","affiliation":[{"name":"School of Computing and Engineering, University of West London, London W5 5RF, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alireza","family":"Esfahani","sequence":"additional","affiliation":[{"name":"School of Computing and Engineering, University of West London, London W5 5RF, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"ref_1","unstructured":"Denizli, A., Alencar, M.S., Nguyen, T.A., and Motaung, D. (2022). Unmanned Aerial Vehicles (UAVs) for Disaster Management. Nanotechnology-Based Smart Remote Sensing Networks for Disaster Prevention, Elsevier."},{"key":"ref_2","first-page":"1","article-title":"Multi-UAV Networks for Disaster Monitoring: Challenges and Opportunities from a Network Perspective","volume":"12","author":"Chandran","year":"2024","journal-title":"Drone Syst. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1109\/COMST.2016.2612223","article-title":"A Survey on Legacy and Emerging Technologies for Public Safety Communications","volume":"19","author":"Kumbhar","year":"2017","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2334","DOI":"10.1109\/COMST.2019.2902862","article-title":"A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems","volume":"21","author":"Mozaffari","year":"2019","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"102739","DOI":"10.1016\/j.jnca.2020.102739","article-title":"Communication and Networking Technologies for UAVs: A Survey","volume":"168","author":"Sharma","year":"2020","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"15460","DOI":"10.1109\/JIOT.2022.3176903","article-title":"UAV-Assisted Data Collection for Internet of Things: A Survey","volume":"9","author":"Wei","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Cao, H., Liu, Y., Yue, X., and Zhu, W. (2017). Cloud-Assisted UAV Data Collection for Multiple Emerging Events in Distributed WSNs. Sensors, 17.","DOI":"10.3390\/s17081818"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2620","DOI":"10.1109\/TNSE.2020.3016569","article-title":"Big Data on the Fly: UAV-Mounted Mobile Edge Computing for Disaster Management","volume":"7","author":"Xu","year":"2020","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1109\/JIOT.2016.2579198","article-title":"Edge Computing: Vision and Challenges","volume":"3","author":"Shi","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"103451","DOI":"10.1016\/j.csi.2020.103451","article-title":"Comprehensive Survey of UAVs Communication Networks","volume":"72","author":"Hentati","year":"2020","journal-title":"Comput. Stand. Interfaces"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1109\/OJCOMS.2020.3010270","article-title":"6G Wireless Communication Systems: Applications, Requirements, Technologies, Challenges, and Research Directions","volume":"1","author":"Chowdhury","year":"2020","journal-title":"IEEE Open J. Commun. Soc."},{"key":"ref_12","unstructured":"LaValle, S.M. (1998). Rapidly-Exploring Random Trees: A New Tool for Path Planning, Department of Computer Science, Iowa State University. Technical Report TR 98-11."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kuffner, J.J., and LaValle, S.M. (2000). RRT-Connect: An Efficient Approach to Single-Query Path Planning. Proceedings 2000 ICRA, Millennium Conference, IEEE International Conference on Robotics and Automation, Symposia Proceedings (Cat. No.00CH37065), IEEE.","DOI":"10.1109\/ROBOT.2000.844730"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1177\/0278364911406761","article-title":"Sampling-Based Algorithms for Optimal Motion Planning","volume":"30","author":"Karaman","year":"2011","journal-title":"Int. J. Robot. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"155215","DOI":"10.1109\/ACCESS.2025.3605761","article-title":"Trajectory and Parameter Optimization in Robust Tracking Control of a Quadrotor","volume":"13","author":"Tran","year":"2025","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"130649","DOI":"10.1016\/j.eswa.2025.130649","article-title":"Trajectory Planning and Tracking for UAVs with Deep Reinforcement Learning and Adaptive Nonlinear MPC","volume":"303","author":"Shi","year":"2026","journal-title":"Expert Syst. Appl."},{"key":"ref_17","unstructured":"Holland, J.H. (1975). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, University of Michigan Press."},{"key":"ref_18","unstructured":"Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kennedy, J., and Eberhart, R. (1995). Particle Swarm Optimization. Proceedings of ICNN\u201995\u2014International Conference on Neural Networks, IEEE.","DOI":"10.1109\/ICNN.1995.488968"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Poudel, S., Arafat, M.Y., and Moh, S. (2023). Bio-Inspired Optimization-Based Path Planning Algorithms in Unmanned Aerial Vehicles: A Survey. Sensors, 23.","DOI":"10.3390\/s23063051"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.1109\/JIOT.2019.2954530","article-title":"Optimal UAV Route in Wireless Charging Sensor Networks","volume":"7","author":"Baek","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jia, Z., Qin, X., Wang, Z., and Liu, B. (2019). Age-Based Path Planning and Data Acquisition in UAV-Assisted IoT Networks. 2019 IEEE International Conference on Communications Workshops (ICC Workshops), IEEE.","DOI":"10.1109\/ICCW.2019.8756751"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4859","DOI":"10.1109\/TWC.2019.2930190","article-title":"Completion Time Minimization for Multi-UAV-Enabled Data Collection","volume":"18","author":"Zhan","year":"2019","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhan, C., and Huang, R. (2019). Energy Minimization for Data Collection in Wireless Sensor Networks with UAV. 2019 IEEE Global Communications Conference (GLOBECOM), IEEE.","DOI":"10.1109\/GLOBECOM38437.2019.9013148"},{"key":"ref_25","first-page":"162","article-title":"Fine-Grained Trajectory Optimization of Multiple UAVs for Efficient Data Gathering from WSNs","volume":"29","author":"Luo","year":"2021","journal-title":"IEEE\/ACM Trans. Netw."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kaul, S., Yates, R., and Gruteser, M. (2012). Real-Time Status: How Often Should One Update?. 2012 Proceedings IEEE INFOCOM, IEEE.","DOI":"10.1109\/INFCOM.2012.6195689"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Abd-Elmagid, M.A., Ferdowsi, A., Dhillon, H.S., and Saad, W. (2019). Deep Reinforcement Learning for Minimizing Age of Information in UAV-Assisted Networks. arXiv.","DOI":"10.1109\/GLOBECOM38437.2019.9013924"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Eldeeb, E., Perez, D.E., de Souza Sant\u2019Ana, J.M., Shehab, M., Mahmood, N.H., Alves, H., and Latva-Aho, M. (2022). A Learning-Based Trajectory Planning of Multiple UAVs for AoI Minimization in IoT Networks. 2022 Joint European Conference on Networks and Communications & 6G Summit (EuCNC\/6G Summit), IEEE.","DOI":"10.1109\/EuCNC\/6GSummit54941.2022.9815722"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Tong, P., Liu, J., Wang, X., Bai, B., and Dai, H. (2019). UAV-Enabled Age-Optimal Data Collection in Wireless Sensor Networks. 2019 IEEE International Conference on Communications Workshops (ICC Workshops), IEEE.","DOI":"10.1109\/ICCW.2019.8756665"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2322","DOI":"10.1109\/COMST.2017.2745201","article-title":"A Survey on Mobile Edge Computing: The Communication Perspective","volume":"19","author":"Mao","year":"2017","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2628","DOI":"10.1109\/TMC.2021.3129785","article-title":"Dynamic Computation Offloading and Server Deployment for UAV-Enabled Multi-Access Edge Computing","volume":"22","author":"Ning","year":"2023","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4232","DOI":"10.1109\/TWC.2022.3224143","article-title":"Multi-UAV Collaborative Sensing and Communication: Joint Task Allocation and Power Optimization","volume":"22","author":"Meng","year":"2023","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.artint.2014.11.006","article-title":"Conflict-Based Search for Optimal Multi-Agent Pathfinding","volume":"219","author":"Sharon","year":"2015","journal-title":"Artif. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Phillips, M., and Likhachev, M. (2011). SIPP: Safe Interval Path Planning for Dynamic Environments. 2011 IEEE International Conference on Robotics and Automation, IEEE.","DOI":"10.1109\/ICRA.2011.5980306"},{"key":"ref_35","first-page":"151","article-title":"Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks","volume":"10","author":"Stern","year":"2019","journal-title":"Proc. Int. Symp. Comb. Search"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Carrese, S., D\u2019Andreagiovanni, F., Nardin, A., Giacchetti, T., and Zamberlan, L. (2021). Seek & Beautify: Integrating UAVs in the Optimal Beautification of E-Scooter Sharing Fleets. 2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), IEEE.","DOI":"10.1109\/MT-ITS49943.2021.9529312"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"103670","DOI":"10.1016\/j.jnca.2023.103670","article-title":"A Survey of UAV-Based Data Collection: Challenges, Solutions and Future Perspectives","volume":"216","author":"Messaoudi","year":"2023","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Alfattani, S., Jaafar, W., Yanikomeroglu, H., and Yongacoglu, A. (2019). Multi-UAV Data Collection Framework for Wireless Sensor Networks. 2019 IEEE Global Communications Conference (GLOBECOM), IEEE.","DOI":"10.1109\/GLOBECOM38437.2019.9014306"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, L., Zhou, S., and Wang, Z. (2022). A Novel Multi-UAV Patrol Path Planning Method Based on Evolutionary Multi-Objective Optimization. 2022 4th International Conference on Intelligent Information Processing (IIP), IEEE.","DOI":"10.1109\/IIP57348.2022.00076"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"108495","DOI":"10.1016\/j.asoc.2022.108495","article-title":"A Decomposition-Based Constrained Multi-Objective Evolutionary Algorithm with a Local Infeasibility Utilization Mechanism for UAV Path Planning","volume":"116","author":"Peng","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ratliff, N., Zucker, M., Bagnell, J.A., and Srinivasa, S. (2009). CHOMP: Gradient Optimization Techniques for Efficient Motion Planning. 2009 IEEE International Conference on Robotics and Automation, IEEE.","DOI":"10.1109\/ROBOT.2009.5152817"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Kalakrishnan, M., Chitta, S., Theodorou, E., Pastor, P., and Schaal, S. (2011). STOMP: Stochastic Trajectory Optimization for Motion Planning. 2011 IEEE International Conference on Robotics and Automation, IEEE.","DOI":"10.1109\/ICRA.2011.5980280"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Mellinger, D., and Kumar, V. (2011). Minimum Snap Trajectory Generation and Control for Quadrotors. 2011 IEEE International Conference on Robotics and Automation, IEEE.","DOI":"10.1109\/ICRA.2011.5980409"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"8227","DOI":"10.1109\/TVT.2019.2923214","article-title":"Intelligent Trajectory Design in UAV-Aided Communications with Reinforcement Learning","volume":"68","author":"Yin","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.jnca.2014.12.002","article-title":"Integration of Cognitive Radio Technology with Unmanned Aerial Vehicles: Issues, Opportunities, and Future Research Challenges","volume":"50","author":"Saleem","year":"2015","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4890","DOI":"10.1109\/TMC.2023.3298888","article-title":"Multi-Objective Optimization for UAV Swarm-Assisted IoT with Virtual Antenna Arrays","volume":"23","author":"Li","year":"2024","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"123023","DOI":"10.1016\/j.eswa.2023.123023","article-title":"Cooperative Task Allocation with Simultaneous Arrival and Resource Constraint for Multi-UAV Using a Genetic Algorithm","volume":"245","author":"Yan","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4305","DOI":"10.1109\/TCOMM.2024.3361536","article-title":"Trajectory Planning and Resource Allocation for Multi-UAV Cooperative Computation","volume":"72","author":"Xu","year":"2024","journal-title":"IEEE Trans. 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