Unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) data collection is a promising solution for timely information acquisition in post-disaster scenarios with damaged terrestrial infrastructure. However, freshness-aware UAV trajectory planning is challenging due to the coupled effects of heterogeneous ground node priorities, Age of Information (AoI) evolution, continuous UAV control, and safety risks caused by no-fly zones and initially unknown obstacles. In this letter, we formulate the safety-constrained weighted AoI minimization problem as a constrained Markov decision process (CMDP) and propose a safety-constrained twin delayed deep deterministic policy gradient (SC-TD3) algorithm with Lagrangian safety optimization to decouple the AoI-oriented objective from long-term safety-risk control and adaptively balance information freshness and safety risk during policy learning. Simulation results show that SC-TD3 achieves higher accumulated reward and reduces mean weighted AoI by 64.3%–78.2% and 67.1%–73.6% in the CN-ratio and GN-scale tests, respectively, while reducing mean total safety cost by 61.4%–75.6% compared with the strongest benchmark algorithm.
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.
Rakan Armoush, Shidrokh Goudarzi, Muhammad Nadeem Khan et al.· Italian National Conference...· 0 citations
Unmanned Aerial Vehicles (UAVs) are pivotal for facilitating data collection in emergency scenarios. Despite the potential of Multi-Agent Deep Reinforcement Learning (MADRL) in coordinating such systems, existing researches struggle to resolve the high-dimensional coupling of data collection, trajectory planning, and energy scheduling under strict collision avoidance and Return-To-Base (RTB) constraints. This paper proposes a energy-aware cooperative MADRL framework designed to maximize data collection utility under energy constraints. Specifically, we employ a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) approach featuring a Centralized Training with Decentralized Execution (CTDE) design and a multi-objective reward mechanism to balance conflicting optimization goals. Extensive simulations validate the advantages of the proposed framework over leading baselines. Notably, the algorithm exhibits significant quantitative advantages in complex high-load scenarios. These outcomes prove that our method achieves higher task completion rates while strictly adhering to RTB and safety protocols.
Jing Mei, Jing-Lei Xu, Zhao Tong et al.· IEEE Transactions on Network...· 0 citations
Maritime unmanned aerial vehicle (UAV) missions such as ship inspection, search and rescue, environmental monitoring, and emergency response often involve multi-wave task releases, time-sensitive deadlines, constrained support vessel positions, and spatially heterogeneous risk. These factors couple task allocation with path planning and make fixed dispatching rules fragile under changing mission profiles. This study develops a hierarchical cooperative planning framework for multiple UAVs over a maritime risk field. A risk-cost A* layer generates feasible routes from support vessels to task points and estimates path length, risk exposure, and sortie duration. A rolling scheduler constructs feasible UAV task candidates, while a scenario-switching-aware LinUCB hyper-heuristic selects online among deadline-first, distance-first, risk-aware, and endurance-balancing rules. A forgetting-update, one-step look-ahead, scenario memory, and lightweight switching detection are used to improve adaptation to mission profile changes. Simulations on a 28 × 40 maritime grid with two support vessels, six UAVs, 40 tasks, and nine release waves show that the proposed framework achieves the highest average effective reward (370.18), the lowest average value regret (0.61), and a best reward ratio of 0.46 over 24 random scenarios. The results should be interpreted as evidence from an idealized simulation benchmark. The main benefit is improved reward robustness under non-stationary and high-risk profiles, rather than uniform gains across all metrics or direct field-deployment validation.
Jian Wu, Shengchang Liu, Wenxi Ni et al.· Drones· 0 citations
Urban fire rescue poses severe challenges to the real-time performance and obstacle avoidance capabilities of unmanned aerial vehicle (UAV) path planning. Existing methods (such as A*, RRT, and standard DQN) have problems such as low search efficiency, insufficient obstacle avoidance ability, or slow convergence in complex environments. This paper proposes an improved deep Q-network (DQN) algorithm, introducing a priority experience replay mechanism to improve sample utilization, and designing a composite reward function including arrival reward, step penalty, direction guidance, and safety penalty to guide the UAV to plan safe and efficient flight paths in complex urban environments. A threedimensional grid simulation environment was constructed based on the real fire incident at Chongqing California Garden. Experimental results show that the improved DQN algorithm outperforms the traditional DQN and RRT algorithms. This method provides a feasible technical solution for multi-UAV collaborative rescue in urban fire scenarios.
Rui Qin, Han-Jin Zhou· International Conference on...· 0 citations
A hierarchical three-dimensional cooperative trajectory-planning framework integrating dynamic-risk-aware Ant Colony Optimization with cooperative Model Predictive Control–Gray Wolf Optimizer (MPC-GWO) is proposed, demonstrating satisfactory safety, coordination, adaptability, and real-time performance in small- to medium-scale simulated urban scenarios.
Yuhan Wang, Pengfei Zhang, Yawen Li et al.· Technologies· 0 citations
In disaster scenarios where terrestrial communication infrastructure is compromised, Unmanned Aerial Vehicles (UAVs) provide a rapid solution for restoring connectivity. However, conventional trajectory planning methods often treat users uniformly, neglecting heterogeneous urgency requirements in emergency environments. This paper proposes an urgencyaware, Quality of Experience (QoE)-driven Deep Reinforcement Learning (DRL) framework for UAV trajectory optimization in multi-priority post-disaster networks. We introduce a configurable priority scaling factor that augments the standardized Mean Opinion Score (MOS) model to explicitly incorporate the influence of urgency on user satisfaction. Additionally, a minimum service-duration mechanism enforces sustained prioritization for critical users. By encoding urgency requirements directly into the satisfaction model, the proposed framework enables the learning-based policy to adaptively and flexibly optimize the trajectory under dynamic disaster conditions. Simulation results demonstrate significant improvements in responsiveness to critical users while maintaining overall network effectiveness.
Zalita Phetxomphou, Hoang D. Le, A. Pham· IEEE International Conferenc...· 0 citations
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