This paper studies 3D multi-UAV path planning and task assignment under uncertain ground PoI demands, and proposes FORTUNE, a hierarchical offline-online framework that consistently outperforms state-of-the-art methods in effectiveness, scalability, and practical applicability.
Abstract
The proliferation of low-altitude intelligent agents is increasing the demand for timely and socially responsible collaborative sensing in dynamic urban environments. However, jointly addressing heterogeneous spatiotemporal demands, environmental uncertainty, and human-centered operational constraints remains challenging. This paper studies 3D multi-UAV path planning and task assignment under uncertain ground PoI demands. Unlike existing work assuming static and fully known PoIs, we model persistent, temporally predictable, and emergent demands within a unified framework. We further incorporate altitude-dependent societal and environmental costs, including noise exposure and public safety risks, to balance sensing performance with socially compliant operations. To solve the resulting large-scale mixed-integer nonlinear problem, we propose FORTUNE, a hierarchical offline-online framework. Offline, a Transformer predicts Type-II PoI activation windows, while an enhanced sparrow search algorithm generates coordinated flight plans through priority-aware decoding and danger-aware evolution. Online, a lightweight refinement module accommodates emerging Type-III PoIs while preserving global mission coherence. Experiments on real-world traffic data and synthetic scenarios show that FORTUNE consistently outperforms state-of-the-art methods in effectiveness, scalability, and practical applicability.
Low-altitude wireless networks (LAWNs) are emerging as a foundational infrastructure for the low-altitude economy, necessitating unmanned aerial vehicles (UAVs) to operate as embodied wireless agents that can navigate complex air-ground environments. This paper investigates the critical challenge of maintaining reliable and covert information transmission for such agents under stringent mobility and detection constraints. To address this, we propose an embodied agentic framework that synergizes safety-critical path planning with cognitive predictive beamforming through a hierarchical execution-reasoning loop. Specifically, the execution layer utilizes model predictive control (MPC) to solve a constrained optimization problem, linearizing obstacle-avoidance requirements into tractable dual-variable inequalities to ensure collision-free navigation. This generates a deterministic sequence of future coordinates that serves as a spatial intent prior to reduce environmental uncertainty. Subsequently, the cognitive reasoning layer leverages a large language model (LLM) to integrate these motion priors with sensing-assisted historical channel state information and warden locations through a cross-modal attention mechanism. By capturing the intricate dependencies between the agent kinematic states and the non-stationary wireless channel, the LLM proactively optimizes predictive beamforming to satisfy covertness requirements against mobile wardens. Simulation results validate the efficacy of this joint design, demonstrating that the UAV successfully tracks reference trajectories while maintaining safety margins in obstacle-cluttered environments. Furthermore, the proposed framework achieves superior covertness performance compared to conventional benchmarks.
Autonomous logistics UAVs operating in low-altitude urban airspace face a fundamentally distinct set of challenges from those in high-altitude flight—extreme obstacle density, regulatory geo-fencing, and unpredictable urban canyon wind shear demand path planners that are simultaneously fast, kinematically smooth, and safety-aware.
Existing grid-based planners (e.g., standard A) generate geometrically suboptimal paths cluttered with sharp 45°/90° turns that waste energy and increase turning workload, while bio-inspired and sampling-based alternatives suffer from stochastic execution times and high hyperparameter sensitivity that preclude deterministic delivery scheduling. Critically, no prior deterministic planner simultaneously minimizes path length, cumulative turning angle, and obstacle proximity risk within a single unified cost function actually enforced during search. Methodology: We propose a hierarchical “Search-and-Smooth” framework comprising: (1) an Enhanced Weighted A algorithm whose edge-cost function directly encodes all three objectives—path length (Jlen), turning cost (Jturn), and spatial risk (
Jrisk
) — and whose inflated heuristic (
w = 1.5
) provides a provable
ε
-suboptimality bound while reducing node expansions; and (2) a greedy Line-of-Sight (LOS) post-processor that removes residual grid-discretization artifacts.
Monte Carlo simulations (
N = 50
randomized urban scenarios, obstacle density
ρobs = 20\%
) demonstrate statistically significant improvements over standard A: computation time reduced by 42.8% (
p
<
0.001
, Wilcoxon signed-rank
W
=
1275
), cumulative turning angle reduced by 69.9% (
p
<
0.001
;
W
=
1275
), and path length maintained within 1.8% of the standard A baseline despite the sub-optimal heuristic inflation. Comparisons against Theta* and a representative PSO baseline confirm the competitive advantages of the proposed method for deterministic, low-latency logistics deployment within the tested synthetic simulation conditions.
Tan Lei, Shi-Ming Liu, Yong-Tao Lei· Frontiers in Robotics and AI· 0 citations
The deployment of high-speed Uncrewed Aerial Vehicles (UAVs) in 3D aerial highways necessitates robust coordination of physical flight kinematics and multi-tier network handovers. While Deep Reinforcement Learning (DRL) offers rapid tactical control, it lacks the zero-shot strategic reasoning required to quickly adapt to dynamic Integrated Terrestrial and Non-Terrestrial Networks (ITNTNs). Conversely, Large Language Models (LLMs) excel at semantic reasoning but suffer from high inference latency, rendering them unsuitable for real-time aerodynamic control. To bridge this gap, we propose a novel Hierarchical LLM-driven control framework. A massive cloud-based LLM deployed on a High-Altitude Platform Station (HAPS) manages slow-timescale global load balancing, while lightweight edge-LLMs on individual UAVs translate local observations into tactical sub-goals. These sub-goals guide a fast-timescale physical DRL controller to execute collision-free, handover-aware trajectories. Simulation results demonstrate that our agentic architecture significantly reduces collision rates and improves aggregate system throughput compared to existing baselines.
Zijiang Yan, Hao Zhou, W. Jaafar et al.· arXiv.org· 0 citations
Traditional target-oriented task allocation and path planning methods often struggle to balance real-time responsiveness to dynamic task alterations with multi-UAV cooperative operations in complex urban environments under meteorological disturbances. To address these challenges, this paper proposes a dynamic path planning method for low-altitude Unmanned Aerial Vehicles (UAVs) tailored for urban inspection missions. Integrating an improved Discrete Particle Swarm Optimization (DPSO) algorithm with a decoupled Soft Actor–Critic (SAC) and B-spline smoothing framework, the proposed approach optimizes upper-level task allocation and lower-level trajectory planning within a 3D joint meteorological-obstacle feasible region. For task scheduling, an improved DPSO algorithm embedded with a spatial topology guidance mechanism dynamically coordinates task flows governed by Poisson processes. effectively addressing the spatial blindness and fragmented route assignments typical of conventional discrete optimization. Concurrently, local trajectory replanning executes receding-horizon spatial exploration via SAC deep reinforcement learning, followed by B-spline refinement to strictly enforce UAV kinematic limits, systematically bridging continuous-space exploration with low-level flight compliance to overcome the kinematic infeasibility common in pure learning-based models. Validated through extensive Monte Carlo comparative simulations (N=50) and further verified by a high-fidelity AirSim dynamic physics engine, the results demonstrate that: (1) The improved DPSO constrains the average response latency for high-priority emergency tasks to within 40 s even under 50 concurrent dynamic tasks. (2) The lower-level replanning achieves an average execution time of 3.60±0.18 s and a path success rate of 95.8±1.2%, in numerical tests, while maintaining a 96.2% kinematic feasibility rate under realistic rigid-body inertia and aerodynamic drag. While the current 3.60 s latency presents a potential bottleneck for millisecond-level dynamic emergency reactions, the developed framework offers a highly effective and safe closed-loop dynamic scheduling solution that lays a rigorous computational foundation for low-altitude urban inspections.
Changqi Yang, Hongjie Hu, Yi Ai· Drones· 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
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
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