Cargo Edge Position Detection from Point-Cloud Frequency Gaps
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Solution Overview
Problem
Existing unmanned transport vehicles struggle to accurately determine cargo handling positions on non-fixed platforms due to varying truck sizes and orientations, requiring high computational power and specific adjustments for each target object, making the method less versatile.
Innovation Solution
A position identification system using a point group acquisition part that irradiates light to acquire data, analyzed via frequency distribution to identify regions of no frequency as cargo placing spaces and edges adjacent to these spaces, allowing for versatile and efficient cargo handling position determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If clustering and pattern matching methods are used to extract target objects from point group data, then measurement precision of edge position is improved, but device complexity and computational power requirements increase
Solution Approach 1:
The patent extracts only the essential feature (frequency distribution characteristics) from the complex point group data, rather than processing all raw data through clustering and pattern matching. This extraction approach maintains measurement precision while significantly reducing computational complexity by focusing only on the most relevant data characteristics.
Solution Approach 2:
The patent replaces complex mechanical data processing operations (clustering algorithms, pattern matching) with a simpler frequency analysis approach. This substitution reduces computational power requirements while maintaining the ability to accurately identify edge positions through frequency distribution patterns.
2Measurement precision
If clustering and pattern matching algorithms are used for target object extraction, then measurement precision is improved, but productivity decreases due to high computational requirements
Solution Approach 1:
The patent extracts only the frequency distribution characteristics from point group data, avoiding computationally intensive clustering and pattern matching operations. This extraction maintains cargo handling position accuracy while reducing processing time, thereby improving cargo handling efficiency.
Solution Approach 2:
The patent changes the analysis parameter from complex spatial relationships (used in clustering) to frequency distribution characteristics. This parameter transformation simplifies the computational task while preserving the ability to accurately determine cargo handling positions, thus improving processing speed and productivity.
3Measurement precision
If determination algorithms are adjusted for each target object to achieve accurate detection, then measurement precision is improved, but adaptability decreases
Solution Approach 1:
The patent creates a universal frequency distribution analysis method that can detect edge positions for various target objects without requiring object-specific algorithm adjustments. This universal approach maintains detection accuracy across different objects while significantly improving method versatility and ease of application.
Solution Approach 2:
The patent transforms the detection approach into a parameter-based frequency analysis that naturally adapts to different objects. By changing from object-specific geometric algorithms to a general frequency distribution method, the system achieves both accurate detection and high adaptability to various cargo configurations and truck types.
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 easy and versatile identification of cargo handling positions by analyzing frequency distributions, reducing computational requirements and enhancing the accuracy of cargo placement and retrieval on varying truck platforms.
Implementation Method 1
a point group acquisition part that horizontally irradiates light into a loading space to acquire a point group
Data Source
AI summary
A position identification system includes a point group acquisition part 22 that horizontally irradiates light into a space above a loading platform Ta to acquire a point group PG, an analysis part, and a position identification part. The analysis part analyzes the acquired point group PG using frequency distribution with a distance in the X-axis direction as an axis. The position identification part identifies a region D2 with substantially no frequency as a cargo placing space based on an analysis result of the point group PG, and identifies sections S1 and S2 with a predetermined frequency or more adjacent to the cargo placing space as the positions of edges in the X-axis coordinate of an object adjacent to the cargo placing space.