Transport Container Partitioning for Stable Mixed-Object Loading
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The challenge of efficiently loading dissimilar objects into transport containers while ensuring stability, safety, and optimizing density is computationally complex and time-consuming, particularly in environments with varying object attributes and robotic constraints.
Innovation Solution
A system and method that utilizes vision systems, machine learning models, and physics engines to determine optimal partitioning, sequencing, and arrangement of objects into transport containers, considering constraints such as weight distribution, robotic capabilities, and time limits, using real-time and offline simulations to generate loading plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If objects are carefully selected and loaded to ensure stability and prevent damage, then object safety and stability are improved, but loading time and operational efficiency deteriorate
Solution Approach 1:
The system performs preliminary computation of optimal loading arrangements using machine learning models before actual loading occurs. The partitioning and arrangement plans are determined in advance based on object characteristics, container specifications, and stability requirements, allowing rapid execution during the actual loading process without compromising stability.
Solution Approach 2:
The system uses vision systems to create digital representations (copies) of physical objects, capturing their dimensions, weight, and other characteristics. These digital models are then used in computational simulations and optimization algorithms to determine stable loading arrangements, eliminating the need for trial-and-error physical arrangements.
2Manufacturing precision
If computational models optimize loading arrangements for stability and efficiency, then loading quality is improved, but computational complexity and processing time increase
Solution Approach 1:
The system implements a tiered optimization approach where a 'good-enough' solution is achieved through faster, less computationally intensive models for time-critical decisions, while more precise optimization is applied when time permits. This partial optimization strategy balances solution quality with computational time constraints.
Solution Approach 2:
The system introduces machine learning models as intermediary components between raw object data and loading decisions. These models pre-process and extract relevant features, transforming complex object characteristics into simplified representations that can be quickly evaluated by optimization algorithms, thereby reducing overall computational complexity.
3Quantity of substance
If dissimilar objects are packed efficiently to maximize container utilization, then space utilization is improved, but weight distribution and stability become more difficult to maintain
Solution Approach 1:
The system applies different placement strategies to different regions of the container based on local requirements. Heavy objects are strategically positioned in the lower regions and near the center for stability, while lighter objects fill upper and peripheral spaces. Each location receives tailored placement instructions based on its specific structural and stability requirements.
Solution Approach 2:
The optimization algorithm dynamically adjusts placement parameters such as position, orientation, and stacking order based on the cumulative weight distribution. As objects are added to the container, the system recalculates optimal parameters for subsequent objects to maintain proper weight balance and stability, transforming static placement rules into adaptive parameter optimization.
4Adaptability or versatility
If traditional loading methods are used without partitioning, then device complexity is reduced, but loading flexibility and adaptability to different object types deteriorate
Solution Approach 1:
The system implements a universal partitioning framework that can handle diverse object types through a single integrated platform. The machine learning models and optimization algorithms are designed to accommodate various object characteristics (dimensions, weight, fragility) and container types without requiring separate specialized systems, achieving multi-functionality through software-based adaptability.
Solution Approach 2:
The partitioning system is designed to be dynamic rather than static. Partition configurations are automatically adjusted based on the specific combination of objects and containers being handled. The system adapts partition locations, sizes, and arrangements in real-time according to the loading requirements, transforming fixed partition structures into flexible, situation-aware configurations.
Data Source
AI summary
The present application discloses a method, system, and computer system for partitioning a set of objects among a set of transport containers. The method includes (i) receiving an indication of the set of objects to be loaded into the set of transport containers, (ii) determining, based at least in part on object information corresponding to the set of objects and resources available to load the set of objects, an plan to partition the set of objects across the set of transport containers, the plan including an indication of a subset of the objects allocated to a corresponding subset of the containers, and (iii) providing the plan as an output to be used in connection with loading various subsets of objects to corresponding subsets of containers.