Armoush, Rakan, Goudarzi, Shidrokh ORCID: https://orcid.org/0000-0003-0383-3553, Khan, Muhammad Nadeem and Esfahani, Alireza (2026) Integrated trajectory planning, MEC offloading, and safety coordination for multi-uav disaster response. Sensors, 26 (17).

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Abstract

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.

Item Type:	Article
Identifier:	10.3390/s26175544
Keywords:	Unmanned Aerial Vehicles (UAVs); disaster response; aerial data collection; 3D-TSPN; Age of Information (AoI); AoI-aware genetic algorithm; RRT-Connect; Lyapunov optimisation; binary MEC offloading; queue stability; 3D path planning; multi-UAV coordination; collision monitoring
Subjects:	Construction and engineering > Aerospace engineering Social sciences > Public welfare > Emergency management > Disaster response Computing > Intelligent systems
Date Deposited:	07 Sep 2026
Dates:	Date Publication status 28 August 2026 Accepted 31 August 2026 Published
School, department or research centre:	School of Computing and Engineering
Keywords:	Unmanned Aerial Vehicles (UAVs); disaster response; aerial data collection; 3D-TSPN; Age of Information (AoI); AoI-aware genetic algorithm; RRT-Connect; Lyapunov optimisation; binary MEC offloading; queue stability; 3D path planning; multi-UAV coordination; collision monitoring
URI:	https://repository.uwl.ac.uk/id/eprint/15291

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