Area-Wide Object Dimensioning for In-Motion Space Optimization
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Solution Overview
Problem
Conventional systems face challenges in efficiently measuring and optimizing the space utilization of objects of varying shapes and sizes due to the lack of accurate dimensioning during transport and storage, particularly in confined areas with limited space and obstacles.
Innovation Solution
An area wide object dimensioning system that uses sensors mounted on vehicles and stationary structures to capture data from multiple perspectives, allowing for real-time determination of object dimensions, shape, volume, and orientation while the object is in motion, enabling flexible and accurate measurement without requiring static positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional dimensioning systems are used for objects in motion, then measurement capability is limited, but measurement precision deteriorates due to lack of accurate dimensioning during transport
Solution Approach 1:
The dimensioning system is segmented into multiple independent sensors positioned at different locations within the area. Each sensor captures data from a specific perspective, and the control circuitry integrates these segmented measurements to reconstruct complete object dimensions, achieving precise measurement without requiring a single complex monolithic sensor system.
Solution Approach 2:
The system transitions from single-point or single-plane measurement to multi-dimensional measurement by deploying sensors at multiple locations throughout the area. This spatial dimensionality allows the system to capture object dimensions from multiple perspectives simultaneously, enabling accurate 3D reconstruction of objects in motion without increasing individual sensor complexity.
2Measurement precision
If static positioning is required for accurate measurement, then measurement precision improves, but productivity deteriorates due to loss of time during positioning
Solution Approach 1:
Multiple sensors are pre-positioned at strategic locations within the area before objects enter. This preliminary arrangement ensures that objects pass through a pre-configured measurement field, eliminating the need for objects to stop or reposition themselves. The measurement infrastructure is prepared in advance to capture dimensions during natural object movement.
Solution Approach 2:
The system is designed to measure objects in motion rather than requiring static positioning. The control circuitry dynamically processes data from multiple sensors to track and reconstruct object dimensions as objects move through the area, maintaining measurement precision while enabling continuous operation without interruption for positioning.
3Measurement precision
If multiple sensors are deployed throughout the area, then measurement precision improves through multiple perspectives, but device complexity increases
Solution Approach 1:
Each sensor in the network is designed with universal functionality to detect and measure multiple object parameters (dimensions, shape, orientation) from its specific location. This multi-functionality reduces the need for specialized sensors for different measurement tasks, simplifying the overall system architecture while maintaining high measurement precision through multi-perspective data collection.
Solution Approach 2:
The control circuitry acts as an intermediary that receives raw data from multiple distributed sensors and processes it into accurate object dimension information. This intermediary processing layer coordinates the data from various sensors, reconciling measurements from different perspectives and locations to produce precise dimensional data without requiring direct complex interactions between individual sensors.
4Volume of moving object
If area wide sensing is implemented, then space optimization improves through accurate dimensioning, but loss of information increases due to data integration challenges
Solution Approach 1:
The control circuitry implements feedback processing where measurements from multiple sensors are continuously cross-validated and integrated. The system uses the redundant information from multiple perspectives to verify and refine object dimension data, ensuring that space optimization calculations are based on accurate dimensional information while minimizing data loss through iterative validation.
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
Enhances space optimization in transport and storage by reducing errors in dimension estimation, optimizing transport routes, and improving billing accuracy through precise object dimensioning, thus increasing efficiency and reducing unused space.
Implementation Method 1
One or more sensors (e.g., a radar system, an acoustic sensor, an image capture system, a LIDAR system, a microwave system, etc.) are located within the area to capture data corresponding to one or more dimensions of the object
Implementation Method 2
One or more sensors (e.g., a radar system, an acoustic sensor, an image capture system, a LIDAR system, a microwave system, etc.) are located within the area to capture data corresponding to one or more dimensions of the object
Data Source
AI summary
The present disclosure provides an area wide object dimensioning system for an object in motion, such as mounted to a vehicle (e.g., a lift truck). One or more sensors (e.g., a radar system, an acoustic sensor, an image capture system, a LIDAR system, a microwave system, etc.) are located within the area to capture data corresponding to one or more dimensions of the object as it travels through the area. Control circuitry receives the data from the sensors, which is converted into multiple dimensions corresponding to one or more surfaces of the object. Surface dimensions are employed to determine a shape, volume, orientation, or area of the surfaces of the object, and/or the object itself, based on the multiple surface dimensions.