Learning to Optimize UAV Path Planning for Data Sensing in Wireless Sensor Networks

• URL: http://arxiv.org/abs/2609.16629v1
• Date: Tue, 15 Sep 2026 04:51:47 GMT
• Title: Learning to Optimize UAV Path Planning for Data Sensing in Wireless Sensor Networks
• Abstract summary: We propose a novel learning-assisted planning framework, termed Landscape-Aware Meta Differential Evolution (LAMDE)<n>LAMDE achieves state-of-the-art performance on the tested complex UAV path planning tasks in WSN data collection scenarios.
• Score: 15.490344146407798
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: UAVs have emerged as highly flexible platforms for data sensing in Wireless Sensor Networks (WSNs). Path planning for UAVs in such tasks plays a key role to assure remote sensing effectiveness and friendly energy consumption. However, existing approaches show two key limitations: i) they are primarily hand-crafted with certain design biases that harm adaptation on unseen tasks. ii) they predominantly assume idealized spatial complexities of actual environments through simplified simulation, causing them to underperform during real-world deployment. In this paper, we propose a novel learning-assisted planning framework, termed Landscape-Aware Meta Differential Evolution (LAMDE), to tackle the mentioned limitations. The major contributions come from the following aspects. We first re-formulate such UAV path planning problem to embrace challenging constraints. To efficiently navigate this highly constrained space, we propose a bi-level learning to optimize approach, where the meta-level is a trainable algorithm configuration policy that meta-learns an adaptable planning strategy for low-level planning algorithm. To address the potential training data scarcity and distribution shift in real-world environments, we introduce a landscape-aware automatic augmentation scheme that enriches training data. At the low-level, a Differential Evolution algorithm is deployed for solving the path planning tasks. To enhance the solving flexibility, we further design a variable-length encoding strategy that dynamically prunes redundant hover points and optimizes continuous flight parameters concurrently within a unified search space. Based on all proposed designs, we meta-train LAMDE and compare it with representative baselines. Comprehensive experiments demonstrate that LAMDE achieves state-of-the-art performance on the tested complex UAV path planning tasks in WSN data collection scenarios.

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