How a Drone Can Fly Ahead of a Moving Vehicle

This patent describes a system where a drone flies a non-linear path, deliberately spaced from a moving ground or water vehicle, using the vehicle's future path information to stay ahead or in a specific relative position.

What it covers

This system involves a 'leading drone' that flies a non-linear path relative to a 'base station' which is a vehicle moving on the ground or water. The base station automatically sends its planned non-linear route and future location to the drone (Claim 1). The drone then calculates and follows its own non-linear path, maintaining a deliberate distance from the base station in real-time (Claim 1). For example, the drone might fly ahead of the base station, reaching a future location while the base station is still lagging behind (Claim 2). The drone can also collect sensor data, like with a directional radar, and update geographical maps as it flies (Claims 8, 10, 15).

What it doesn't cover

• —Does not cover systems where the base station is an airborne vehicle; it must be on the ground or water surface (Claim 1).
• —Does not cover scenarios where the base station or the drone follows a straight-line path; both paths must be non-linear (Claim 1).
• —Does not cover a drone that flies directly above the base station's path; its path must be parallel but not directly 'attitudinally above' (Claim 5).
• —Does not cover a base station that manually provides its path information; it must automatically send this data (Claim 1).
• —Does not cover drones that simply follow the base station without maintaining a deliberate, pre-calculated spacing along a relative path.

The clever bit

The core innovation is the drone's ability to anticipate and proactively position itself relative to a moving base station by using the base station's *future* path information, rather than simply reacting to its current position or passively following it.

Why it matters

This technology is important for applications where a drone needs to scout ahead or perform tasks in advance of a slower-moving vehicle. It allows for proactive data collection or mission execution, rather than reactive following. This could be useful in fields like surveying, security, or environmental monitoring, enabling more efficient operations.

Real-world examples

• 1.Autonomous agricultural vehicles using drones for crop scouting ahead of planting or harvesting.
• 2.Self-driving cars or trucks with accompanying drones for advanced road condition scanning.
• 3.Autonomous marine vessels deploying drones for ahead-of-ship navigation or environmental surveys.
• 4.Security patrols using ground robots with leading drones for perimeter surveillance.