2026· IEEE Transactions on Cognitive Communications and Networking· Vol 12, pp. 11062-11073· 0 citations· 31 references
Computer Science
Abstract
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.
This paper addresses autonomous intervention with an underwater vehicle--manipulator system (UVMS) in confined, cluttered, and partially known environments, where poor maneuverability, narrow passages, and uncertain execution may cause the robot to enter unrecoverable regions. We propose MANTA, a three-layer hierarchical planning-and-control framework that couples passage accessibility, manipulation feasibility, and closed-loop execution. The first layer performs global connectivity reasoning in a conservative reduced base space to extract traversable corridor candidates toward the task region. The second layer refines each candidate corridor by jointly optimizing the continuous base motion and arm trajectory, producing a collision-free base--arm trajectory. The third layer learns a reach-and-hold base policy using Gaussian-process model-based reinforcement learning (MBRL) through MC-PILCO, enabling trajectory tracking and station keeping at the planned manipulation state. During execution, the framework monitors map updates and can trigger recovery and route repair when the active passage becomes infeasible. MANTA is evaluated in confined UVMS planning and closed-loop tracking experiments. Across 120 matched planning queries, it achieves higher task success than full-state sampling-based baselines while producing larger clearance margins and lower arm motion. The learned MC-PILCO policy further reduces position and yaw tracking errors on both training and unseen tube-like references. These results show MANTA as a structured and data-efficient framework for safe autonomous underwater intervention in caves, tubes, and cluttered subsea structures.
Mohamed Abdelwahab, Ruggero Carli, Damiano Varagnolo et al.· 0 citations
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.
Minghui Liwang, Wenhan Jia, Xinlei Yi et al.· 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
To address the challenge that single algorithms struggle to balance global exploration and local obstacle avoidance, and are prone to falling into local optima in complex environments, this paper proposes a Strategic Hierarchical Path Planning (SHPP) framework. This framework decouples the 3D navigation task into three synergistic layers. The top layer employs a reinforcement learning network equipped with a Credit Alignment Mechanism (CAM) to provide macroscopic guidance, eliminating credit assignment pollution and escaping local minima. The middle layer introduces a Dual-Guided Particle Swarm Optimization (DG-PSO) algorithm to map discrete commands into continuous smooth trajectories. The bottom layer executes physical collision avoidance and tracking based on the Artificial Potential Field (APF) method. Simulations indicate that the system can establish a stable policy in approximately 625 episodes, achieving an average reward of 95.6. Furthermore, benefiting from the hierarchical architecture's smooth optimization in continuous space, the average flight path length is 138.3 meters, a reduction of approximately 19% compared to traditional discrete decision-making models. These quantitative results fully validate the superior performance of the proposed architecture in complex 3D environments.
Fei Wang, Jun-Yong Shi, Zhao-Kun Chen et al.· International Conference on...· 0 citations
Unmanned aerial vehicles (UAVs) have emerged as a key enabler of next-generation Internet of Things (IoT) ecosystems, offering flexible aerial relaying to extend connectivity across dynamic vehicular ad hoc networks (VANETs) in smart city environments. However, conventional centralized approaches for UAV trajectory planning require continuous global network state aggregation, making them impractical under bandwidth and energy constraints typical of dense urban deployments. In this article, we present TRUAV, a distributed multi-agent reinforcement learning framework based on independent tabular Q-learning for joint UAV trajectory planning and routing enhancement in UAV-aided VANETs. Each UAV is equipped with a local Q-learning agent that operates purely on locally observable information, including vehicle density, packet queue states, and neighbor UAV positions, thereby eliminating the need for global state exchange. A potential-game-inspired reward design encourages spatial diversity and routing-aware UAV positioning among interacting agents while accounting for energy consumption. Numerical simulations over a large urban area with 200 mobile vehicles show that the proposed TRUAV framework achieves network coverage and packet delivery ratios comparable to centralized deep reinforcement learning methods, while also improving relay delay and energy efficiency. Finally, we discuss emerging challenges and future research directions for distributed multi-agent UAV-assisted IoT systems.
Muhammad Umar Farooq Qaisar, Lin Zhang, Zhen Chen et al.· arXiv.org· 0 citations
Most UAV-based transmission line inspection systems still use preset waypoints and treat task planning as separate from motion control. Single-layer methods, such as waypoint-based, learning-based or MPC-based methods, generally handle planning or control independently; none coordinate task reallocation with trajectory adjustment in the event of wind disturbances or sensor faults. We propose a hierarchical framework that connects task-level plans with motion-level control via intelligent agents operating at two different timescales. A high-level strategic agent divides the task into multiple areas. Several low-level execution agents plan local trajectories and coordinate via a distributed consensus protocol. A conditional value-at-risk (CVaR)-aware model predictive controller is employed for motion control to address wind disturbances, obstacle constraints and actuator limits; fault detection activates an adaptive replanning mechanism to maintain operation under degraded sensor conditions or communication loss. In simulation on a 15-km corridor with three UAVs, the framework achieved 15.7% higher inspection coverage than conventional waypoint-based methods, 17.5% lower energy consumption, and a tracking error of less than 0.35 m at crosswinds of up to 12 m/s. Multi-UAV coordination was validated in simulation; a single-UAV field experiment on a 110 kV line segment validated autonomous inspection with 93.3% coverage and approximately 0.5 m positioning accuracy.
Huanyu Cheng, Yingcheng Gu, Mengting Xi et al.· IEEE Access· 0 citations
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