Transport Vehicle Edge Detection for Off-Position Cargo Handling
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Solution Overview
Problem
Conventional unmanned guided vehicles fail to accurately detect the position of mobile shelves or trucks that deviate from predetermined positions, leading to cargo handling inefficiencies and potential loading issues due to a 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 frequency distributions to specify edge positions, allowing for precise edge detection and distance calculation between cargo and surrounding objects without relying on shape recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional shape detection methods are used to detect cargo or objects, then detection flexibility with respect to position and posture is improved, but versatility is worsened because different characteristic values and techniques are required for different objects
Solution Approach 1:
The patent extracts only the essential edge position information from objects by analyzing frequency distributions of point groups, rather than detecting complete shapes. This extraction approach allows uniform processing of diverse objects (pallets, cargo, mobile shelves, trucks) by focusing on their common edge characteristics, thereby improving versatility while maintaining detection flexibility
Solution Approach 2:
The patent changes the detection parameter from complete shape recognition to edge position detection through frequency distribution analysis. By transforming the detection task from identifying object characteristics to measuring distance frequencies, the system achieves universal applicability across different object types while maintaining operational flexibility
2Measurement precision
If cargo handling position is determined on the assumption that mobile shelf or truck will deviate, then detection of loaded cargo is improved, but loading efficiency is worsened because cargo cannot be loaded with space closed
Solution Approach 1:
The patent implements real-time feedback by continuously measuring actual edge positions of mobile shelves and trucks using point group frequency distribution, then using this feedback to dynamically adjust cargo handling positions. This eliminates the need for conservative predetermined positions while ensuring accurate loading, thereby improving both measurement precision and loading efficiency
Solution Approach 2:
The patent performs preliminary edge position detection before cargo handling operations, allowing the system to pre-calculate optimal handling positions based on actual object locations. This preliminary action enables efficient loading without requiring excessive clearance, as the system is already aware of the actual positions of mobile shelves and trucks
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 edge positions and gap distances, enabling efficient cargo handling even when mobile shelves or trucks deviate from predetermined positions, improving loading precision and versatility.
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
Figure 1
Figure 2
Figure 3A~3C
AI summary
A transport vehicle capable of detecting the position of a surrounding object without detecting the shape itself is provided. The transport vehicle (1) includes a point group acquisition unit (22) and an edge specifying unit (35). The point group acquisition unit acquires a point group (PG) by horizontally irradiating cargo loaded on a cargo loading unit (16) 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.