Multi-UAV Trajectory Planning Parameters for Offshore Wind Farms

figshare hosts parameters for a neural network-based deep reinforcement learning method used in multi-UAV cooperative inspection. The dataset, authored by Yunqi Liao, supports a distributed differential game framework for energy-aware trajectory planning. It is a small tabular dataset stored in an XLS file of 5.5 KB.

Use Cases

• Validate the convergence to a global Nash equilibrium using the provided NN-DRL method parameters.
• Reproduce energy-aware trajectory planning simulations for multi-UAV systems in offshore wind farms.
• Analyze parameters related to round-trip mission requirements and energy consumption constraints.
• Benchmark inspection efficiency and task completion time against conventional approaches using the supplied method data.

Strengths

• Dataset is openly licensed under CC BY 4.0.
• Focuses on a specific application: multi-UAV cooperative inspection in offshore wind farms.
• Method parameters are derived from a published framework incorporating energy consumption and round-trip requirements.

Limitations

• Extremely small scale at 5.5 KB, indicating very limited parameter data.
• No sample data or column definitions are available, hindering immediate analysis.
• Data scope is confined to the parameters of a single proposed method, not empirical results.

Provenance

Source

figshare, author Yunqi Liao.

Collection Method

Parameters from a proposed NN-DRL method for a distributed differential game framework.

Time Range

null

Freshness

Last updated March 2026.

Geography

Application context is offshore wind farms.