Air Mobility Collision Avoidance Using ADS-B Risk Zones
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Solution Overview
Problem
There is a need for technology to enhance flight stability and ensure safe avoidance flights of air mobility systems, particularly unmanned aircraft, without requiring new system construction, as the number of unmanned aircraft increases and autonomous flight technology advances.
Innovation Solution
An apparatus and method utilizing an automatic dependent surveillance-broadcast (ADS-B) device, sensor devices such as radar and camera, and a processor to determine risk zones and perform avoidance flights by modifying flight paths based on surveillance and sensor information, allowing for both cooperative and non-cooperative avoidance strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensor devices (radar, camera) are added to improve detection accuracy for avoidance targets, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple sensor devices (ADS-B receiver, radar, camera) into an integrated sensor system that shares common processing resources and coordinate systems. The processor fuses data from all sensors to detect avoidance targets, allowing the system to achieve high detection accuracy while managing complexity through unified architecture rather than separate independent systems.
Solution Approach 2:
The processor performs multiple functions: it processes ADS-B surveillance information, processes radar signals, processes camera images, determines risk zones, predicts flight paths, and controls avoidance maneuvers. This multi-functional approach eliminates the need for separate dedicated systems for each function, improving detection precision while controlling overall system complexity.
2Reliability
If real-time surveillance information processing is implemented to improve flight safety, then reliability is improved, but use of energy increases
Solution Approach 1:
The system performs preliminary processing of surveillance information by pre-determining risk zones based on the first air mobility's flight path and characteristics before searching for avoidance targets. This preliminary action reduces the computational burden during real-time operation, as the processor only needs to check if potential targets fall within pre-calculated risk zones rather than analyzing all possible trajectories.
Solution Approach 2:
The system focuses computational resources on processing only the surveillance information and sensor data relevant to the current risk zone and flight path, rather than continuously analyzing all possible directions and parameters. This selective processing maintains high flight safety while reducing overall energy consumption compared to exhaustive analysis of all environmental data.
3Extent of automation
If autonomous avoidance flight control is implemented to improve flight stability, then extent of automation is improved, but device complexity increases
Solution Approach 1:
The processor autonomously determines whether avoidance flight is necessary by analyzing surveillance information and sensor data, automatically predicts the flight path of avoidance targets, decides on avoidance maneuvers, and controls the first air mobility without external intervention. This self-service capability achieves high automation while managing complexity through integrated decision-making algorithms that combine multiple functions in a single processing unit.
4Measurement precision
If risk zone determination based on flight path analysis is performed to improve avoidance accuracy, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system determines risk zones in advance based on the first air mobility's flight path, speed, and altitude characteristics before actively searching for avoidance targets. This preliminary determination of risk zones allows the processor to quickly assess whether detected targets require avoidance maneuvers, improving avoidance accuracy while minimizing real-time processing time by pre-establishing evaluation criteria.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables safe and accurate avoidance flights for various air mobilities, regardless of their type or piloted status, by leveraging existing systems and optimizing flight paths using ADS-B and sensor data, without the need for additional infrastructure.
Implementation Method 1
an automatic dependent surveillance-broadcast (ADS-B) device that is mounted on a first air mobility to receive first surveillance information
Implementation Method 2
a sensor device that is mounted on the first air mobility to obtain sensor information for searching for the second air mobility
Data Source
AI summary
An embodiment apparatus for controlling an air mobility includes an ADS-B device on a first air mobility to receive first surveillance information of the first air mobility and second surveillance information of a second air mobility, a sensor device on the first air mobility to obtain sensor information for searching for the second air mobility, one or more processors, and a storage device storing a program to be executed by the one or more processors, the program including instructions to determine a risk zone on a first flight path of the first air mobility based on the first surveillance information, determine whether the second air mobility is an avoidance target located in the risk zone based on the second surveillance information or the sensor information, and perform an avoidance flight of the first air mobility in response to a determination that the second air mobility is the avoidance target.