Threshold-Based Clustering for Real-Time Object Proximity Detection

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Solution Overview

Problem

Existing proximity detection systems are resource-intensive, time-consuming, and limited to specific dimensional spaces, lacking efficiency and economic feasibility for real-time collision avoidance in various applications.

Innovation Solution

A threshold-based clustering method using Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) to reduce search space, followed by brute force processing, with a configurable processing unit for real-time object collision detection in 2D, 3D, or N-dimensional spaces.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If brute force approach is used to calculate distance between each object continuously, then collision detection accuracy is improved, but processing time and resource consumption increase significantly

Engineering Contradiction:

Improvecollision detection accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent segments the continuous space into discrete grid cells, where each cell represents a spatial region. Objects are assigned to grid cells based on their positions, and collision detection is performed by comparing objects within the same or adjacent grid cells rather than calculating distances between all object pairs. This segmentation reduces the computational complexity from O(n²) to approximately O(n), significantly improving processing efficiency while maintaining collision detection accuracy.

Inventive Principle:

Principle #1Segmentation

2Measurement precision

If brute force approach is used to calculate distance between each object continuously, then collision detection accuracy is improved, but resource consumption increases significantly

Engineering Contradiction:

Improvecollision detection accuracyVSAvoidresource consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

Inventive Principle:

Principle #1Segmentation

3Productivity

If QuadTrees are used for collision detection, then processing efficiency is improved, but the system is limited to only 2-Dimensional coordinates

Engineering Contradiction:

Improveprocessing efficiencyVSAvoiddimensional space limitation

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The patent implements a universal grid-based collision detection system that can operate in any dimensional space (2D, 3D, or N-dimensional) by using a hash map to store and retrieve grid cells. The system maintains a single data structure that adapts to different dimensions, eliminating the need for separate QuadTree (2D) or OctTree (3D) implementations. This provides both high processing efficiency and universal adaptability across different dimensional spaces.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Productivity

If OctTrees are used for collision detection, then processing efficiency is improved, but the system is limited to only 3-Dimensional systems

Engineering Contradiction:

Improveprocessing efficiencyVSAvoiddimensional space limitation

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

Inventive Principle:

Principle #6Universality (Multi-functionality)

5Adaptability or versatility

If user requires collision detection in N dimensional space, then system versatility is improved, but using multiple specialized structures (QuadTree + OctTree) increases complexity and cost

Engineering Contradiction:

Improvedimensional space flexibilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12443680B2System and method for detecting proximity between objects

Publication Date: 2025.10.14 ALGHAITH MOHAMMAD SAUD M

AI summary

A method is provided for detecting proximity between objects using threshold based clustering. The method includes receiving object data of a plurality of objects in a space to be monitored, from one or more data capturing devices disposed in a space, clustering the object data into one or more clusters, based on a predetermined threshold distance, thereby reducing the search space, and processing the one or more clusters using brute force to identify the plurality of objects which lie within the predetermined threshold distance of each other, thereby identifying the plurality of objects that are at risk of collision.