Sorting Station Package Redistribution With Optimized Robot Loading
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for loading sorted packages into transport units at sorting stations are inefficient, often requiring manual loading and resulting in wasted space within the transport units.
Innovation Solution
A method that involves scanning packages to detect sorting parameters, sorting them into parallel sequences, and using a common robot to load packages into transport units based on optimized sequences determined by a control device, considering both sorting parameters and loading states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual loading is used to load packages into transport units, then loading speed and accuracy are improved, but automation is lost and labor costs increase
Solution Approach 1:
The patent replaces manual mechanical loading operations with an automated robot system. The robot is equipped with sensors and control systems that enable it to automatically grasp, transport, and place packages into transport units based on optimization algorithms, eliminating the need for manual labor while maintaining high loading speed and accuracy.
Solution Approach 2:
The robot system performs self-directed loading operations by autonomously determining the optimal loading sequence and positioning. The system uses real-time data from sensors and optimization algorithms to make independent decisions about package placement without human intervention, enabling the loading process to serve itself.
2Extent of automation
If traditional robot loading is used, then automation is achieved, but space utilization in transport units deteriorates due to wasted space
Solution Approach 1:
The system performs preliminary calculations of the optimal loading sequence before the robot begins loading packages. The optimization algorithm determines the best arrangement of packages in advance, taking into account package dimensions, weights, and destination requirements, ensuring maximum space utilization from the start of the loading process.
Solution Approach 2:
The loading optimization is dynamic and adaptive. The system continuously monitors the loading process and adjusts the optimal sequence in real-time based on the current state of the transport unit, package characteristics, and changing requirements, allowing the robot to adapt its loading strategy to maximize space utilization throughout the entire loading process.
3Productivity
If packages are sorted into multiple parallel sequences, then sorting efficiency is improved, but the complexity of the loading system increases
Solution Approach 1:
The robot system is designed with multi-functionality to handle multiple parallel sorting sequences simultaneously. A single robot can switch between different sequences and transport units, performing multiple functions that would otherwise require separate systems. This universal approach maintains high sorting efficiency while avoiding the complexity of having dedicated robots for each sequence.
Solution Approach 2:
The control system acts as an intermediary that coordinates between multiple parallel sorting sequences and the robot. It manages the complexity by centralizing the decision-making process, optimizing the loading sequence across all sequences, and directing the robot's actions to maintain efficiency without requiring complex modifications to the robot itself.
4Volume of stationary object
If optimized loading sequences are calculated, then space waste is reduced, but the computational effort and time for sequence determination increase
Solution Approach 1:
The system performs preliminary optimization calculations for the loading sequence before the actual loading process begins. By calculating the optimal arrangement in advance based on available package data and transport unit characteristics, the system minimizes the computational burden during the loading process itself, reducing real-time computation time while still achieving maximum space utilization.
Solution Approach 2:
The system uses feedback from the loading process to refine and adjust the optimization sequence. Sensors monitor the actual loading state and package placement, providing real-time data that feeds back into the optimization algorithm. This allows the system to make incremental adjustments without requiring complete recalculation, balancing space optimization with computational efficiency throughout the loading process.
Data Source
Figure 1
Figure 2
AI summary
Described and illustrated is a method for redistributing packages (2) in a sorting station (1). In order to further increase the efficiency of sorting stations with reasonable effort, it is provided that the packages (2) are scanned one after the other in at least one transport sequence (4) to detect at least one sorting parameter in each case according to the at least one transport sequence (4). The scanned packages (2) in the at least one transport sequence (4) are sorted in a sorting device (6) based on the at least one sorting parameter and divided into at least two parallel sorting sequences (10) of packages (2). The packages (2) of the parallel sorting sequences (10) are loaded one after the other into at least one transport unit (12) using at least one common robot (11) according to the sorting parameters.that a control device (6) determines an optimized loading sequence for the at least one common robot (11) based on the at least one sorting parameter of the packages (2) of the at least two parallel sorting sequences (10) and, preferably, based on at least one loading state of the at least one transport unit (12), and that the packages (2) are loaded into the at least one transport unit (12) by the at least one common robot (11) according to the optimized loading sequence.