3D Sensor Package Loading Optimization
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Solution Overview
Problem
Current package loading methods are inefficient, leading to suboptimal use of cubic space in vehicles, resulting in increased loading time and costs due to manual placement and significant empty spaces within the loading area.
Innovation Solution
A package loading system utilizing 3D sensors and optimization engines to determine the optimal placement and orientation of packages within a vehicle, minimizing empty spaces by projecting light onto empty spaces and providing guidance to agents through a user interface, ensuring maximum cubic efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual package placement methods are used, then loading operations can be performed with simple equipment, but loading time increases and cubic efficiency decreases due to significant empty spaces
Solution Approach 1:
The system performs preliminary actions by pre-calculating optimal package placement configurations before actual loading occurs. The optimization engine computes the best arrangement of packages to minimize empty spaces and maximize cubic efficiency, providing guidance instructions to workers before they begin the loading process. This preliminary planning eliminates trial-and-error during actual loading, significantly reducing loading time while improving space utilization.
2Device complexity
If manual package placement is used, then equipment complexity remains low, but cubic efficiency of loading space deteriorates due to suboptimal package arrangement
Solution Approach 1:
The system replaces complex mechanical automated loading mechanisms with an intelligent software-based optimization engine that runs on standard computing hardware. Instead of using sophisticated robotic arms and automated positioning systems, the invention uses algorithms to calculate optimal package arrangements and provides visual guidance to human workers. This substitution maintains low equipment complexity while achieving high cubic efficiency through intelligent software optimization.
3Volume of stationary object
If optimized package placement is implemented, then cubic efficiency improves, but system complexity increases due to introduction of sensors and optimization engines
Solution Approach 1:
The system implements self-service by enabling the loading process to optimize itself through automated calculation and guidance provision. The optimization engine automatically analyzes package dimensions, computes optimal arrangements, and provides step-by-step placement instructions without requiring external expert intervention. The system serves its own optimization needs using readily available package data and standard computing resources, minimizing the need for additional complex infrastructure while achieving improved cubic efficiency.
4Productivity
If traditional loading methods are used, then loading costs remain manageable, but loading efficiency decreases leading to increased time and resource consumption
Solution Approach 1:
The system implements feedback by continuously monitoring the loading process and comparing actual package placement against the optimized arrangement. The system provides real-time guidance to workers, indicating the correct placement locations and orientations of packages. This feedback mechanism ensures that packages are placed according to the optimal configuration, maximizing cubic efficiency and reducing wasted space. The feedback loop enables continuous improvement of loading efficiency while controlling costs through reduced time and resource consumption.
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
The system significantly reduces loading time and costs by maximizing the use of available space within vehicles, ensuring packages are placed in the most efficient manner to minimize empty spaces and enhance cubic efficiency.
Implementation Method 1
obtain sensor data corresponding to a current loading configuration within the vehicle
Implementation Method 2
determine an optimal package placement of incoming packages to be loaded into the vehicle according to the sensor data and package data
Implementation Method 3
projecting light onto empty spaces and providing guidance to agents through a user interface
Data Source
AI summary
Disclosed are various embodiments for optimizing cubic utilization when loading items into a loading space. A current loading configuration of the loading space can be determined according to image data obtained by 3D sensors. Item data (e.g., volume, mass, type, dimensions, etc.) can be determined for incoming items to be loaded into the loading space. The current loading configuration and the item data can be used to determine an item sequence and optimal placement location for the next item to be loaded such that a cubic efficiency of the loading space is maximized and amount of air gaps between items is minimized. The current loading configuration can further be used to determine if a sensing system or an item loading system needs to be repositioned. Whether the next item was placed in the optimal placement can also be verified based on subsequent image data obtained by the 3D sensors.