@Article{cmes.2026.085689,
AUTHOR = {Xiao-Juan Li, Yu Zhang, Xing-She Zhou, Meng-Jie Li, Xin-Yue Liu},
TITLE = {OGU: Near-Optimal Group Selection of Heterogeneous Sensing UAVs via Capability Modeling and Aggregation},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {148},
YEAR = {2026},
NUMBER = {2},
PAGES = {--},
URL = {http://www.techscience.com/CMES/v148n2/68585},
ISSN = {1526-1506},
ABSTRACT = {Sensor-equipped Unmanned Aerial Vehicles (UAVs) are increasingly deployed for collaborative aerial sensing, yet selecting an optimal subgroup from a heterogeneous fleet remains challenging. Existing approaches rank individual UAVs by fixed, isolated metrics (e.g., sensor type, residual energy) and deploy them sequentially, failing to quantify task-specific performance under coupled operational uncertainties arising from platform heterogeneity, sensor configuration, and environmental dynamics. To address this, we propose Near-Optimal Group UAV Selection (OGU), a capability-driven modeling method. Rather than directly manipulating raw, heterogeneous hardware parameters, OGU aggregates each UAV–sensor unit into a capability entity characterized by intrinsic task-oriented attributes that are computed for the specific task context. This reformulation transforms group UAV selection into capability matching, reducing the optimization complexity from a high-dimensional coupled parameter space to a compact capability space. For an area coverage task, each heterogeneous UAV–sensor pair is abstracted as a unified Sweep Capability with two attributes—coverage benefit and energy cost—computed via sub-capability aggregation. An energy-efficient greedy heuristic algorithm then identifies a near-optimal UAV subgroup that fulfills task requirements with minimal total energy expenditure by leveraging these capability attributes. Empirically, the selected subgroup deviates from the exact MILP solution by less than a 5% optimality gap. Extensive simulations demonstrate that OGU outperforms the state-of-the-art selectivity-based Opti-U method and a random-selection baseline. OGU achieves a 131.3% higher energy efficiency and a 11.2% higher area coverage ratio while deploying fewer UAVs and reduces task execution time by approximately 7<math id="mml-ieqn-1"><mo>×</mo></math> through simultaneous group deployment. The proposed capability-oriented paradigm provides a flexible and scalable framework for managing heterogeneous sensing UAVs across diverse tasks.},
DOI = {10.32604/cmes.2026.085689}
}