Rolling Dispatch for AAVs Inspection Based on Task Adaptive Clustering

Abstract

The assignment and scheduling of AAV power inspection tasks constitute a representative Mixed-Integer Nonlinear Programming (MINLP) problem. However, due to the complexity of the workflow, manual methods and existing heuristics struggle to balance time costs and solution quality for large-scale tasks. To address this challenge, this paper introduces a rolling window approach for batch processing and proposes a clustering strategy before scheduling. Building on this strategy, we develop a rolling dispatch algorithm utilizing task-adaptive clustering (RDA-TAC), which alleviates problem complexity and enhances search efficiency. Specifically, we present a novel MINLP model designed to minimize total equivalent endurance losses, incorporating multi-level and sequential inspection constraints derived from actual inspection processes. We then batch and segment large-scale tasks, treating task fragments as the smallest units for assignment, and adaptively adjust them to convert the problem into a single-drone path planning task. Through nine scenarios based on real power grid data, RDA-TAC attains optimal solutions for small-scale tasks and outperforms manual and heuristic methods in more complex cases, demonstrating a tenfold increase in solving speed. Note to Practitioners—Autonomous aerial vehicles (AAVs) are the leading method for power line inspection, offering rapid deployment, cost-effectiveness, and improved safety. However, traditional manual assignment and path planning algorithms often struggle with large-scale and complex tasks. To address this, we propose a strategy that clusters tasks before scheduling, reducing complexity and improving efficiency. Empirical validation demonstrates that our method balances solution time and quality effectively. Comparative results are included in the attached digital format.