Transport Vehicle Edge Detection for Cargo Gap Positioning
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Solution Overview
Problem
Conventional unmanned guided vehicles struggle to accurately detect the position of cargo or objects on mobile shelves or trucks that deviate from predetermined positions, leading to inefficiencies in cargo handling due to lack of versatility in shape detection methods.
Innovation Solution
A transport vehicle equipped with a point group acquisition unit that irradiates objects with light and analyzes the acquired point group using frequency distributions to specify edge positions, allowing for precise detection of object edges without shape recognition, and a distance calculation unit to determine gaps between cargo and nearby objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional shape detection methods are used to detect cargo positions, then detection flexibility is improved, but versatility deteriorates due to dependency on object shape and size
Solution Approach 1:
The patent extracts the essential feature for detection from complex object shapes by focusing only on edge positions. Instead of detecting and analyzing the complete shape of cargo objects, the system extracts and processes only the boundary edge information, which is sufficient for determining cargo positions and gaps. This extraction approach eliminates dependency on object shape and size characteristics, thereby improving versatility while maintaining detection capability.
Solution Approach 2:
The patent changes the detection parameter from comprehensive shape information to specific edge position information. By transforming the detection task from identifying complete object geometries to locating edge boundaries through frequency distribution analysis, the system achieves parameter simplification that enhances adaptability across different cargo types without requiring shape-specific detection algorithms.
2Measurement precision
If cargo handling position is determined assuming deviation, then detection capability is improved, but cargo loading efficiency deteriorates due to inability to close space
Solution Approach 1:
The patent implements a feedback mechanism where the actual edge positions of cargo and nearby objects are detected and used to dynamically adjust the cargo handling position. The system detects edge positions, calculates actual gaps, and uses this feedback information to determine optimal loading positions that fully utilize available space, thereby improving both detection accuracy and loading efficiency simultaneously.
Solution Approach 2:
The patent transitions from static predetermined positioning to dynamic position adjustment based on actual object positions. The cargo handling position is not fixed in advance but is dynamically determined according to the detected edge positions and gap distances, allowing the system to adapt to deviations and optimize space utilization in real-time.
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 accurate detection of object edges and gap distances, enabling efficient cargo handling even when mobile shelves or trucks deviate from predetermined positions, improving versatility and operational efficiency.
Implementation Method 1
a point group acquisition unit that acquires a point group by horizontally irradiating cargo loaded on the cargo loading unit and/or an object around the transport vehicle with light
Data Source
AI summary
A transport vehicle capable of detecting the position of a surrounding object without detecting the shape itself is provided. The transport vehicle includes a point group acquisition unit and an edge specifying unit. The point group acquisition unit acquires a point group PG by horizontally irradiating cargo loaded on a cargo loading unit and an object around the transport vehicle with light. The edge specifying unit analyzes the acquired point group PG using a frequency distribution with distances in left-right and front-rear directions as axes, and specifies sections S1, S2, S3, and S4 with frequencies, adjacent to an area with substantially no frequency, as positions of edges in the left-right direction or the front-rear direction of the cargo and the object around.