The Method of Hierarchical Attention Mechanism-based Path Planning for Multi-UAV Inspection

FEI Bowen; XING Wenjie; LIU Daqian

doi:10.11999/JEIT260192

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FEI Bowen, XING Wenjie, LIU Daqian. The Method of Hierarchical Attention Mechanism-based Path Planning for Multi-UAV Inspection[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260192

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FEI Bowen, XING Wenjie, LIU Daqian. The Method of Hierarchical Attention Mechanism-based Path Planning for Multi-UAV Inspection[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260192

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The Method of Hierarchical Attention Mechanism-based Path Planning for Multi-UAV Inspection

doi: 10.11999/JEIT260192 cstr: 32379.14.JEIT260192

Software College (College of Artificial Intelligence), Liaoning Technical University, Huludao 125105, China

Funds: The National Natural Science Foundation of China (62302509,62303477), Liaoning Province Artificial Intelligence Innovation and Development Plan Project (2023JH26/10300027), The Basic Research Project of Liaoning Provincial Department of Education (LJ212410147090)

Accepted Date: 2026-05-29
Rev Recd Date: 2026-05-29

Available Online: 2026-06-08

Abstract

Abstract

Objective In the domain of modern power grid maintenance, the deployment of multiple Unmanned Aerial Vehicles (UAVs) for efficient and cooperative inspection has become a pivotal yet challenging task. Existing multi-UAV path planning methods often struggle with inadequate cooperative scheduling and fail to accurately capture the complex topological relationships among heterogeneous nodes—specifically between inspection equipment points and charging stations under strict energy constraints. To address these limitations, this paper proposes a novel Hierarchical Attention mechanism-based Path Planning method for multi-UAV power Inspection (HAPPI). The primary objective is to minimize the total flight distance of a UAV fleet while ensuring all devices are inspected and all UAVs safely return to the base, despite dynamic energy consumption and partial environmental observability. Methods The multi-UAV power inspection problem is first formulated as a combinatorial optimization problem with energy constraints and modeled within a Markov Decision Process (MDP) framework. To solve this problem, HAPPI adopts an encoder-decoder architecture powered by a customized hierarchical attention mechanism. The encoder employs a multi-level attention design comprising three dedicated layers: the first layer uses self-attention among all equipment nodes to learn their spatial proximity and visitation preferences; the second layer applies cross-attention between equipment nodes and charging stations to model their energy supply-demand relationships; and the third layer utilizes self-attention among charging stations to explicitly capture the topological structure of the charging network. This hierarchical design enables the model to discern functional differences and dependencies among heterogeneous nodes effectively. The decoder integrates the global graph embedding, the embedding of the last visited node, and the UAV's current remaining energy to generate a context vector. Through a single-head attention mechanism, it computes compatibility scores for all candidate nodes, followed by a masking strategy that invalidates infeasible nodes (e.g., visited nodes, unreachable nodes, or nodes that would strand the UAV). The final node selection follows a probability distribution derived via softmax, supporting both greedy and sampling-based decoding strategies. The policy network is trained using a reinforcement learning framework with a baseline network to stabilize training, optimizing the parameters via policy gradient to minimize the expected total path length (Fig. 2). Results and Discussions Extensive simulations are conducted across three problem scales: T20C2 (20 devices, 2 stations), T60C6 (60 devices, 6 stations), and T100C10 (100 devices, 10 stations). The training process shows that HAPPI achieves faster convergence and lower final cost compared to baseline Attention Model (AM) and Heterogeneous Attention-based Deep Reinforcement Learning (HADRL) methods (Fig. 4). In comprehensive performance comparisons, HAPPI (sampling) obtains the shortest total path lengths on T60C6 (6.21) and T100C10 (8.41), outperforming five classical meta-heuristic algorithms and the two deep reinforcement learning baselines (Table 3). Notably, HAPPI reduces total path length by approximately 12% on average compared to the baselines. Visualization of planned paths demonstrates that HAPPI generates routes with less crossover and more balanced workload distribution among UAVs, enhancing both safety and efficiency (Fig. 6, Fig. 7). The single-UAV path length distribution further confirms HAPPI's superior load-balancing capability across all scales (Fig. 8). Conclusions This paper presents HAPPI, a novel hierarchical attention mechanism-based deep reinforcement learning method for cooperative path planning in multi-UAV power inspection scenarios with multiple charging stations. By explicitly modeling the spatial relationships among equipment points, the energy dependencies between equipment and charging stations, and the internal topology of the charging network, HAPPI effectively addresses the shortcomings of existing methods in information aggregation and constraint satisfaction. Experimental results across various scales that HAPPI achieves superior planning quality, higher computational efficiency, and stronger generalization compared to state-of-the-art heuristic and learning-based algorithms. Future work will extend the framework to multi-objective optimization incorporating time, risk, and energy trade-offs, and validate the method with real-world inspection data.
- Power inspection,
- Multi-UAV,
- Path planning,
- Hierarchical attention mechanism,
- Explicit learning

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References(28)

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