Battery-logistics-aware operations planning for UAV-based scanning of large built environments
Ruopeng Huang, Kaijian Li, Wenshun Wang, Yuguo Zhang, Shuai Feng, Jie Li, Xinyue FuPurpose
This study develops a battery-logistics-aware operations planning framework for unmanned aerial vehicle (UAV)-based scanning of large built environments, addressing the gap between geometrically feasible missions and executable field operations under coupled resource, time and cost constraints.
Design/methodology/approach
The framework converts spatial scanning demand into battery-feasible sorties, coordinates UAVs, reusable batteries, chargers, couriers and personnel, and evaluates UAV-battery-courier configurations through cost-time Pareto optimization. Bounded exact enumeration is benchmarked against five multi-objective methods across 300 cases and applied to the Xuzhou Daguozhuang project through a web prototype.
Findings
Bounded exact enumeration recovered the complete reference Pareto front across 300 cases with an average runtime of 0.0315 s. In the project application, 36.5% of the recommended makespan arose from nonflight processes. The balanced configuration reduced makespan by 74.2% relative to the lowest-cost plan while reducing cost by 68.1% relative to the fastest plan, demonstrating the importance of battery circulation, logistics and bottleneck migration.
Practical implications
The framework helps practitioners identify operational bottlenecks, prioritize battery-related resource preparation and select Pareto-efficient configurations according to project time, budget and resource constraints.
Originality/value
Unlike UAV planning studies that typically begin with predefined missions or treat batteries mainly as endurance constraints, this study links endogenous workload generation, cyclic battery-logistics coordination and schedule-dependent cost-time resource planning within one field-operations framework.