Transport Container Loading Plans for Stable Weight Distribution
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Solution Overview
Problem
The challenge of efficiently loading dissimilar objects into transport containers while ensuring stability, safety, and optimal weight distribution is computationally complex and time-consuming, particularly in environments where robotic systems are used, due to variations in object attributes and constraints such as robotic resource limitations and container dimensions.
Innovation Solution
A system and method that utilizes vision systems and machine learning models to optimize the partitioning, sequencing, and arrangement of objects within transport containers, considering constraints like time, safety, and resource availability, using simulations and heuristics to determine efficient loading plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If objects are carefully selected and loaded to ensure stability and proper weight distribution, then object damage and container collapse are avoided, but the loading process becomes time-consuming and computationally complex
Solution Approach 1:
The system performs preliminary computation of loading plans before actual loading occurs. The optimization algorithm calculates the optimal arrangement of objects considering weight distribution, stability, and container constraints in advance, allowing the robotic system to execute pre-determined placement sequences without real-time decision delays.
Solution Approach 2:
The system creates a digital twin or virtual model of the container and objects to simulate and optimize loading arrangements. By working with a computational representation rather than physical objects during the planning phase, the system can evaluate multiple scenarios rapidly and select the optimal loading plan before execution.
2Manufacturing precision
If the system accounts for variations in object attributes and container constraints, then loading accuracy and safety improve, but computational complexity increases
Solution Approach 1:
The system transforms the complex loading optimization problem into a parameter-based mathematical model. By representing objects and containers with key parameters (dimensions, weight, center of gravity) and formulating constraints as mathematical inequalities, the system enables efficient computation using optimization algorithms rather than exhaustive simulation.
Solution Approach 2:
The loading problem is divided into discrete decision variables corresponding to individual object placements. The system segments the continuous optimization space into discrete placement options for each object, allowing the use of combinatorial optimization techniques and integer programming to find optimal solutions efficiently.
3Productivity
If robotic systems are used to automate loading, then labor efficiency improves, but adaptability to varying object types and container configurations decreases
Solution Approach 1:
The optimization system is designed as a universal platform that can handle diverse object types and container configurations through a unified mathematical formulation. The system accepts various input parameters representing different object attributes and constraint types, and automatically adapts the optimization model to the specific problem instance without requiring specialized algorithms for each case.
Solution Approach 2:
The system dynamically adjusts the optimization parameters and constraints based on the specific objects and containers being loaded. Rather than being hardwired for specific configurations, the system reconfigures its computational model in real-time to match the actual loading scenario, enabling flexible adaptation to varying requirements.
Data Source
AI summary
The present application discloses a method, system, and computer system for arranging a set of objects for loading to a transport container. The method includes (i) receiving an indication of the set of objects to be loaded into the transport container for transport from a source location to a destination location, (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 arrangement of the set of objects loaded into the transport container that satisfies a weight distribution criteria, and (iii) providing the arrangement as an output to be used in connection with loading the set of objects to the transport container.