UAV Autonomous Landing via 3D Evidence Grid and Radar
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Solution Overview
Problem
Existing vision-based methods for autonomous UAV landing on sea-based ships face challenges in adverse weather and environmental conditions, such as wind, rain, snow, and deck motion, which hinder the identification of high-contrast landmarks, leading to unreliable navigation and landing.
Innovation Solution
The method employs onboard sensors to create a three-dimensional evidence grid, combining sensor data with priori information to locate and validate landing zones, characterizing deck motion, and generating flight controls for safe landing, independent of ship-based guidance signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If vision-based methods using high-contrast landmarks are used for autonomous landing, then the landing guidance can be simplified, but the system becomes unreliable in adverse weather conditions such as wind, rain, snow, and deck motion
Solution Approach 1:
The patent replaces vision-based optical detection with a radar-based electromagnetic detection system. The radar system transmits electromagnetic waves and processes the reflected signals to detect deck landmarks and determine position, thereby substituting the mechanical/optical vision system with an electromagnetic wave-based system that is insensitive to adverse weather conditions
Solution Approach 2:
The patent changes the detection parameter from optical contrast (visibility of high-contrast landmarks) to electromagnetic wave reflection characteristics. By using radar cross-section and signal reflection properties instead of visual contrast, the system maintains reliability in adverse weather where optical detection fails
2Difficulty of detecting and measuring
If traditional vision-based approaches relying on high-contrast landmarks are used, then the landmark identification is straightforward, but the system fails when environmental factors obscure or distort visual signals
Solution Approach 1:
The patent replaces the vision-based detection system with a radar-based electromagnetic detection system. The radar transmitter sends electromagnetic waves toward the deck landmark, and the radar receiver processes the reflected signals to identify the landmark's position and characteristics, eliminating dependence on visual contrast and environmental lighting conditions
Solution Approach 2:
The patent introduces electromagnetic waves as an intermediary medium between the UAV and the deck landmark. Instead of direct visual detection that is blocked by weather, the radar system uses electromagnetic wave transmission and reflection as a mediator to convey landmark information through adverse environmental conditions
3Measurement precision
If ship-based guidance signals are used for landing, then the landing precision can be improved, but the UAV becomes dependent on external guidance infrastructure
Solution Approach 1:
The patent enables the UAV to perform self-service navigation by equipping it with onboard radar transmitters and receivers. The UAV independently transmits electromagnetic waves, receives reflections from the deck and surrounding environment, processes the signal data, and determines its own position and landing trajectory without requiring external ship-based guidance infrastructure
Solution Approach 2:
The patent makes the radar system multi-functional by using the same electromagnetic wave transmission and reception mechanism for both navigation (detecting deck landmarks and determining position) and obstacle detection (identifying objects on the deck). This universal system replaces multiple specialized systems and eliminates dependence on external guidance while maintaining precision
Data Source
AI summary
A method for autonomous landing of an unmanned aerial vehicle (UAV) comprising: obtaining sensor data corresponding to one or more objects outside of the aircraft using at least one onboard sensor; using the sensor data to create a three dimensional evidence grid, wherein a three dimensional evidence grid is a three dimensional world model based on the sensor data; combining a priori data with the three dimensional evidence grid; locating a landing zone based on the combined three dimensional evidence grid and a priori data; validating an open spots in the landing zone, wherein validating includes performing surface condition assessment of a surface of the open spots; generating landing zone motion characterization, wherein landing zone motion characterization includes characterizing real time landing zone pitching, heaving, rolling or forward motion; processing the three dimensional evidence grid data to generate flight controls to land the aircraft in one of the open spots; and controlling the aircraft according to the flight controls to land the aircraft.