Collision Candidate Detection for Moving Objects
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Solution Overview
Problem
The increasing number of moving objects, such as drones, autonomous vehicles, and satellites, poses a challenge in efficiently and accurately detecting potential collision risks, especially with limited monitoring capabilities.
Innovation Solution
A method and apparatus that determine collision candidate objects by analyzing position-related information across multiple time points to identify common objects at risk of collision with a tracking target object, utilizing angular ranges and elliptical orbit existence areas based on gravitational fields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the number of moving objects to be monitored increases, then the coverage of collision detection improves, but the complexity of the monitoring system and computational load increase
Solution Approach 1:
The patent segments the monitoring process into multiple stages: initial collision risk assessment, detailed analysis of identified candidates, and prioritization based on risk levels. This multi-stage segmentation allows the system to handle large numbers of objects by focusing computational resources only on potentially problematic pairs rather than analyzing all possible combinations.
Solution Approach 2:
The patent introduces intermediate parameters and criteria as mediators between the large set of moving objects and the final collision assessment. These intermediaries include preliminary risk indicators, positional relationship metrics, and prioritization scores that filter and organize the data before detailed analysis, reducing the complexity of direct comprehensive monitoring.
2Measurement precision
If comprehensive monitoring of all moving objects is performed, then collision detection accuracy improves, but the time and computational resources required increase
Solution Approach 1:
The patent performs preliminary actions by conducting initial assessments of collision risk using simplified criteria and metrics before committing to detailed analysis. Objects that pass preliminary screening thresholds are then subjected to more rigorous analysis, while those that fail are quickly eliminated. This preliminary filtering action significantly reduces the time required for comprehensive monitoring while maintaining accuracy for high-risk cases.
Solution Approach 2:
The patent applies partial action by focusing detailed monitoring efforts only on a subset of objects that meet certain risk criteria, rather than uniformly monitoring all objects with the same level of detail. This selective partial monitoring maintains high detection accuracy for problematic cases while reducing overall computational time and resource consumption.
3Measurement precision
If detailed analysis is performed on all potential collision candidates, then detection accuracy improves, but the computational load and processing time increase
Solution Approach 1:
The patent applies local quality by varying the level of analysis detail according to the specific characteristics and risk profile of each candidate object pair. High-risk candidates receive detailed localized analysis with multiple parameters, while lower-risk candidates receive streamlined assessment. This local differentiation of analysis quality optimizes computational resource allocation while maintaining overall detection accuracy.
Solution Approach 2:
The patent changes parameters dynamically based on the assessment stage and candidate risk level. Different sets of parameters are applied at different stages: initial screening uses fewer parameters, while detailed analysis of high-risk candidates incorporates additional parameters. This parameter adaptation reduces computational load by avoiding unnecessary parameter calculations for low-risk cases while ensuring thorough analysis when needed.
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
This approach enables reliable monitoring of collision candidate objects with high accuracy, reducing unnecessary monitoring and allowing for swift collision risk prevention measures across a large number of moving objects.
Implementation Method 1
determining a collision candidate object for a tracking target object among a plurality of objects moving along an elliptical orbit based on a gravitational field from a reference point
Data Source
AI summary
Provided is a method for determining a collision candidate object for a tracking target object. The method comprises obtaining position-related information for a tracking target object at a target time point, determining a first collision candidate object group based on position-related information on each object at a first time point prior to the target time point and position-related information on the tracking target object at the target time point, determining a second collision candidate object group based on position-related information on each of a plurality of objects at a second time point prior to the first time point and position-related information on the tracking target object at the target time point, and determining at least one object included in both the first collision candidate object group and the second collision candidate object group as a collision candidate object for the tracking target object at the target time point.