Voronoi Diagram Collision Prediction for Moving Bodies
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Solution Overview
Problem
Current methods for predicting collisions between multiple moving bodies are inefficient, often requiring significant computational resources and may miss actual collisions due to reliance on sampling methods and coordinate system dependencies, especially when dealing with multiple moving bodies in dynamic environments.
Innovation Solution
A method utilizing Voronoi diagrams to model moving bodies as two-dimensional or three-dimensional geometric shapes, calculating edges or faces, and tracking flipping and collision events to predict collisions with reduced computational complexity, allowing for accurate collision detection and conflict analysis across various dimensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sampling methods are used to predict collisions between multiple moving bodies, then collision detection can be performed, but the calculation amount increases greatly and may fail to detect actual collisions
Solution Approach 1:
The patent segments the collision detection problem by dividing the monitoring time window into multiple time slots and organizing moving bodies into groups based on their spatial relationships. This segmentation allows the system to process collision predictions in a structured manner, reducing the overall calculation burden while maintaining detection accuracy through systematic analysis of body interactions within each segment.
Solution Approach 2:
The patent introduces a hierarchical dimension to the collision detection process by organizing bodies into groups and subgroups based on spatial proximity. This dimensional organization transforms the problem from a flat O(N²) comparison approach to a multi-level structure where comparisons are first made within groups, then between groups, significantly reducing the number of required calculations while preserving detection reliability.
2Reliability
If sampling frequency is increased to reduce missed collision detection, then collision detection reliability improves, but calculation amount increases greatly requiring large-scale supercomputers
Solution Approach 1:
The patent applies preliminary action by pre-organizing moving bodies into groups and subgroups based on their spatial relationships before performing collision detection. This pre-organization creates a structured framework that enables efficient processing during the actual detection phase, allowing the system to maintain high reliability without requiring increased sampling frequency or excessive computational power.
Solution Approach 2:
The patent implements dynamics by adaptively adjusting the grouping structure and time slot divisions based on the actual distribution and movement patterns of the moving bodies. This dynamic adaptation allows the system to optimize its computational resources in real-time, maintaining high detection reliability while minimizing the required computational power by focusing calculations only where collisions are most likely to occur.
3Adaptability or versatility
If conventional collision prediction methods are used, then collision detection can be performed, but the methods depend on coordinate systems and are specialized only for two moving bodies
Solution Approach 1:
The patent achieves universality by creating a coordinate-system-independent grouping methodology that can handle any number of moving bodies in any spatial configuration. The group-based approach naturally extends from two bodies to multiple bodies without requiring fundamental changes to the method, making it universally applicable while managing complexity through systematic organization of bodies into hierarchical groups.
Solution Approach 2:
The patent applies inversion by reversing the conventional approach: instead of comparing each pair of bodies directly in a coordinate system, it first groups bodies by spatial proximity and then performs comparisons within these groups. This inverted approach eliminates coordinate system dependency and naturally scales to any number of bodies, reducing method complexity while improving adaptability.
Data Source
AI summary
Disclosed is a method for predicting collisions and conflicts between multiple moving bodies. A method for predicting and avoiding collisions and conflicts between multiple moving bodies comprises the steps of: creating objects by modeling the shape of each of multiple moving bodies; creating two-dimensional circles by modeling the objects by using size information of the objects; modeling the two-dimensional circles into moving disks by using at least one of the moving speeds of the moving bodies, the monitoring time window for the moving bodies, and the size information of the two-dimensional circles; computing a Voronoi diagram between the moving disks and calculating edges of the Voronoi diagram; and during the monitoring time window for the moving bodies, calculating a flipping event in which at least one of the edges of the Voronoi diagram is converted into a vertex and then converted into another edge, and a collision event by which a collision between a pair of moving disks defining an edge of the Voronoi diagram is predicted, and calculating whether actual collisions occur between moving disks triggering the flipping event and between moving disks triggering the collision event, in chronological order of the occurrence of the flipping event and the collision event.