Loading Platform Position Identification via Point-Group Frequency Analysis
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Solution Overview
Problem
Unmanned transport vehicles face challenges in determining cargo handling positions on variable-sized loading platforms of trucks due to inconsistent truck parking and cargo placement, requiring high computational power and specific adjustments for each object, making the method less versatile.
Innovation Solution
A position identification system using a point group acquisition part to irradiate light, analyze frequency distribution, and identify regions with no frequency as cargo placing spaces, and edges with a predetermined frequency as cargo handling positions, simplifying the process and reducing computational requirements.
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 (edge position) from the point group data by projecting points onto the X-axis and identifying frequency peaks, rather than performing comprehensive clustering and pattern matching on all point cloud features. This extraction approach maintains measurement precision while significantly reducing computational complexity.
Solution Approach 2:
The patent replaces complex computational algorithms (clustering and pattern matching) with a simpler frequency analysis method based on X-axis projection. This substitution reduces the computational burden while maintaining the ability to accurately detect edge positions of objects on the loading platform.
2Measurement precision
If clustering and pattern matching methods are used to extract target objects, then measurement precision is improved, but ease of operation deteriorates due to need for adjustment for each object
Solution Approach 1:
The patent creates a universal method that works for different object types (cargo, pallets, etc.) by using frequency distribution analysis on X-axis projections. The method automatically adapts to various objects without requiring specific adjustments, making it versatile while maintaining accurate edge position detection.
Solution Approach 2:
The patent changes the approach from object-specific parameter adjustment to a general frequency-based parameter analysis. By analyzing the frequency distribution of projected points, the system automatically determines edge positions for any object type, eliminating the need for manual adjustment and improving ease of operation.
3Measurement precision
If high data processing power is used to perform clustering and pattern matching, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The patent extracts only the necessary information (X-axis coordinates of points) and performs simple frequency analysis, rather than processing the entire point cloud data with energy-intensive clustering algorithms. This extraction and simplified analysis maintain measurement precision while significantly reducing energy consumption.
Solution Approach 2:
The patent uses a computationally inexpensive frequency analysis method that consumes minimal processing energy compared to complex clustering algorithms. The method achieves sufficient measurement precision with much lower energy requirements, making it suitable for resource-constrained autonomous vehicles.
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 point groups with frequency distribution, reducing computational complexity and enhancing the versatility of cargo handling operations.
Implementation Method 1
a point group acquisition part that horizontally irradiates light into a loading space to acquire a point group
Data Source
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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 (303), and a position identification part (305). 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 (S 1 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.