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lssue 12, 2025
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Yunhe Pan, Aiguo Fei
Advisor Xicheng Lu
ISSN 2095-9184
CN 33-1389/TP
IF (2024 JCR) 2.9
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Volume 26 Issue 12，2025
Special Feature on Engineering and Technology for Low-Altitude Economy Infrastructure
“In the field of artificial intelligence, expert Dr. Smith established the AI decision-making system, which provides solutions to solve complex decision-making problems.”Special Feature on Engineering and Technology for Low-Altitude Economy Infrastructure
SPID: a deep reinforcement learning-based solution framework for siting low-altitude takeoff and landing facilities# Enhanced Publication AI Introduction
“In the field of urban air mobility, this study introduces a deep reinforcement learning-based solution framework, SPID, which significantly enhances solution efficiency and robustness for siting low-altitude takeoff and landing platforms. Expert xx established the SPID system, which provides solutions to solve vertiport siting problems under flight and capacity constraints.”Abstract：Siting low-altitude takeoff and landing platforms (vertiports) is a fundamental challenge for developing urban air mobility (UAM). This study formulates this issue as a variant of the capacitated facility location problem, incorporating flight range and service capacity constraints, and proposes SPID, a deep reinforcement learning (DRL)-based solution framework that models the problem as a Markov decision process. To handle dynamic coverage, the designed DRL framework-based SPID uses a multi-head attention mechanism to capture spatiotemporal patterns, followed by integrating dynamic and static information into a unified input state vector. Afterward, a gated recurrent unit (GRU) is used to generate the query vector, thereby enhancing sequential decision-making. The action network within the DRL network is regulated by a loss function that integrates service distance costs with unmet demand penalties, enabling end-to-end optimization. Subsequent experimental results demonstrate that SPID significantly enhances solution efficiency and robustness compared with traditional methods under flight and capacity constraints. Especially, across the social performance metrics emphasized in this study, SPID outperforms the suboptimal solutions produced by traditional clustering and graph neural network (GNN)-based methods by up to approximately 29%. This improvement comes with an increase in distance-based cost that is kept within 10%. Overall, we demonstrate an efficient, scalable approach for vertiport siting, supporting rapid decision-making in large-scale UAM scenarios.Keywords：Low-altitude planning;Vertiport siting;Deep reinforcement learning;Algorithm exploration53|50|0Updated：2026-01-12Special Feature on Engineering and Technology for Low-Altitude Economy Infrastructure
Eixão-UAM: LLM-assisted iterative design of a low-altitude urban air mobility corridor in Brasilia Enhanced Publication AI Introduction
“Reporting on the cutting-edge advancements in urban air mobility, this study introduces its research progress in the field of low-altitude airspace planning. Expert researchers established the Eixão-UAM system, which provides solutions to solve scalable, regulation-aware planning problems and lays a foundation for the construction of future low-altitude airspace systems.”Abstract：The development of urban air mobility (UAM) systems requires scalable, regulation-aware planning of low-altitude airspace and supporting infrastructure. This study proposes an end-to-end framework for the design, simulation, and iterative optimization of a structured UAM corridor over Brasilia’s central road axis (Eixão-UAM), aligned with the Brazilian unmanned aircraft traffic management (BR-UTM) ecosystem. In addition, this study proposes a multilayered aerial configuration stratified by unmanned aerial vehicle class, supported by a modular ground infrastructure composed of vertihubs, vertiports, and vertistops. A takeoff-scheduling simulator is developed to evaluate platform allocation strategies under realistic traffic and weather conditions. Initial experiments compare a round-robin (RR) baseline with a genetic algorithm (GA), and results reveal that RR outperforms GA v1 in terms of the average waiting time. To address this gap, a large language model (LLM) assisted optimization loop is implemented using GPT-4o Mini and Gemini 2.5 Pro. The LLMs act as reasoning partners, supporting the root-cause diagnoses, fitness function redesign, and rapid prototyping of five GA variants. Among these, GA v5 achieves a 59.62% reduction in maximum waiting time and an approximately 10% reduction in average waiting time over GA v1, thereby approaching the robustness of RR. In contrast, GA v2–v4 and GA v6 perform less consistently, showing an importance of fitness function design. These results underscore the role of an iterative, LLM-guided development in enhancing classical optimization, demonstrating that generative artificial intelligence (AI) can contribute to simulation acceleration and the cocreation of operational logic. The proposed method provides a replicable blueprint for integrating LLMs into early-stage UAM planning, offering both theoretical insights and architectural guidance for future low-altitude airspace systems.Keywords：Brasilia;Eixão;Genetic algorithm;Large language model (LLM);Unmanned aerial vehicle (UAV);Urban air mobility (UAM);UAM corridor;Unmanned aircraft traffic management (UTM)24|32|0Updated：2026-01-12Special Feature on Engineering and Technology for Low-Altitude Economy Infrastructure
Coevolutionary genetic programming for large-scale dynamic multi-aircraft task allocation AI Introduction
“In the field of multi-aircraft task allocation, this paper introduces a novel coevolutionary genetic programming framework that automatically designs high-performance reactive heuristics for dynamic MATA problems. Expert established the CoGP system, which provides solutions to solve complex and dynamic rescue environment problems.”Abstract：Multi-aircraft task allocation (MATA) plays a vital role in improving mission efficiency under dynamic conditions. This paper proposes a novel coevolutionary genetic programming (CoGP) framework that automatically designs high-performance reactive heuristics for dynamic MATA problems. Unlike conventional single-tree genetic programming (GP) methods, CoGP jointly develops two interacting populations, i.e., task prioritizing heuristics and aircraft selection heuristics, to explicitly model the coupling between these two interdependent decision phases. A comprehensive terminal set is constructed to represent the dynamic states of aircraft and tasks, whereas a low-level heuristic template translates developed trees into executable allocation strategies. Extensive experiments on public benchmark instances simulating post-disaster emergency delivery demonstrate that CoGP achieves superior performance compared with state-of-the-art GP and heuristic methods, exhibiting strong adaptability, scalability, and real-time responsiveness in complex and dynamic rescue environments.Keywords：Task allocation;Genetic programming (GP);Hyperheuristic;Combinatorial optimization;Learn-to-optimize29|45|0Updated：2026-01-12Special Feature on Engineering and Technology for Low-Altitude Economy Infrastructure
Integrating the cat’s eye effect and deep learning for low-altitude target detection Enhanced Publication AI Introduction
“In the field of low-altitude target detection, this paper proposes an active low-altitude target detection method based on the cat's eye effect, which provides solutions to address the current limitations in detecting low, slow, and small unmanned aerial vehicles. Expert xx established the detection system, which incorporates a control module, a laser emission component, a co-optical path panoramic scanning optical mechanism structure, an echo reception component, target detection, and visualization processing to achieve small target detection.”Abstract：This paper addresses the urgent need to detect low, slow, and small (LSS) unmanned aerial vehicles (UAVs) in complex and critical environments, proposing an active low-altitude target detection method based on the cat’s eye effect. The detection system incorporates a control module, a laser emission component, a co-optical path panoramic scanning optical mechanism structure, an echo reception component, target detection, and visualization processing to achieve small target detection. The light source is emitted by a near-infrared laser, and the scanning optical path is realized using micro-electro-mechanical system (MEMS) mirrors and servo mechanisms. The echo reception signal is received by an avalanche photodiode (APD) and the target detection module, which captures the reflected signal and distance information. The detection software integrates the local pyramid attention (LPA) module and the field pyramid network (FPN) through the UAV micro lens identification algorithm. It eliminates false alarms by incorporating SKNet21 and uses the APD to collect echo intensity and flight time, thereby reducing the false alarm rate. The results demonstrate the feasibility of the proposed target detection method, which achieves a mean average precision of 0.809 at an intersection over union (IoU) of 0.50, a mean average precision of 0.324 at an IoU of 0.50–0.95, and a throughput of 49.8 Giga floating-point operations per second (GFLOPs), indicating that it can address the current limitations in LSS target detection.Keywords：Low-altitude detection;Optical path detection;Cat’s eye effect;SKNet21;Local pyramid attention;Average precision46|50|0Updated：2026-01-12Special Feature on Engineering and Technology for Low-Altitude Economy Infrastructure
Integrated communication–sensing–navigation–control for low-altitude digital-intelligent networks: architecture, enabling technologies, and experimental validation Enhanced Publication AI Introduction
“In the field of low-altitude economy, this paper proposes a low-altitude digital-intelligent network (LADIN) system, which provides solutions to solve the problems of deeply integrated communication, sensing, navigation, and control capabilities.”Abstract：The rapid advancement of the low-altitude economy (LAE) necessitates a fundamental shift from fragmented systems toward deeply integrated communication, sensing, navigation, and control capabilities. To this end, this paper proposes a low-altitude digital-intelligent network (LADIN) as an overarching architecture, with integrated sensing and communication (ISAC) serving as the core enabling technology that pervasively unifies its three layers. At the heterogeneous infrastructure layer, we detail an ISAC waveform design based on orthogonal frequency division multiplexing, enabling dual-purpose hardware to simultaneously achieve high-speed data transmission and high-precision environmental sensing. Within the intelligent data fusion layer, ISAC’s role expands into a multimodal fusion paradigm, providing the crucial electromagnetic sensing modality. This layer constructs a unified spatiotemporal feature space by introducing pluggable back-projection adapters and spatiotemporal modeling. These adapters systematically integrate heterogeneous data from ISAC, optical cameras, and light detection and ranging (LiDAR) by inverting their respective observation models, thereby overcoming representational disparities and association ambiguities. At the service and management layer, this coherent representation directly drives algorithmic processes and control policies. ISAC resources are virtualized into dynamically allocable assets, enabling closed-loop control that responds to the real-time state of the feature space, such as reconfiguring base station operational modes based on live situational awareness. Validation through multi-frequency collaborative sensing and multimodal fusion use cases demonstrates significant performance gains in tracking robustness, detection of near-zero radar cross-section targets such as balloons, and seamless urban airspace governance, conclusively establishing the transformative potential of a deeply integrated, ISAC-centric approach for future LAE systems.Keywords：Low-altitude economy;Integrated sensing and communication;Airspace management;Uncrewed aerial vehicles;Cyber-physical system50|47|0Updated：2026-01-12
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