Server-Controlled Multi-Order Picking for Grouped Robot Loading Boxes
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Solution Overview
Problem
Conventional warehouse shipment transportation relies heavily on manual labor, and integrating robots with warehouse management systems for efficient order distribution is challenging.
Innovation Solution
A server system that sorts and groups orders based on shipment volume and location, allocates groups to robots one-to-one, and assigns tasks to robots for simultaneous multi-order picking, reducing the need for separate distribution and limiting worker movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple orders are allocated to a single robot, then productivity increases through simultaneous order distribution, but device complexity increases due to the need for sorting and grouping mechanisms
Solution Approach 1:
The server divides the warehouse into multiple zones and segments orders into different groups based on their destination locations. Each robot is assigned a specific zone and handles only orders within that zone, creating a segmented distribution system that improves productivity while managing complexity through spatial division
Solution Approach 2:
The server acts as an intermediary that performs centralized sorting and grouping of orders before allocation. It receives all order information, processes it through sorting algorithms, and then分配s optimized routes to robots, mediating between the WMS and individual robots to manage system complexity
2Productivity
If orders are sorted by shipment volume and location, then picking efficiency improves, but loss of time increases due to additional sorting processing
Solution Approach 1:
The server performs preliminary sorting and grouping of orders by shipment volume and storage location before robots begin their picking tasks. By pre-organizing orders into optimized groups with predetermined routes, the system eliminates the need for robots to make real-time decisions, improving picking efficiency while the sorting occurs in advance
Solution Approach 2:
The server dynamically changes sorting parameters such as grouping thresholds, route optimization criteria, and zone boundaries based on real-time warehouse conditions, shipment characteristics, and robot availability. This adaptive parameter adjustment optimizes picking efficiency for different scenarios while managing processing time
3Ease of manufacture
If robots are allocated tasks based on grouped orders, then labor costs decrease through automation, but device complexity increases due to robot coordination requirements
Solution Approach 1:
Each robot is equipped with autonomous navigation and task execution capabilities, allowing it to independently navigate to picking stations, retrieve shipments, and return to loading boxes without constant human intervention. The robot serves itself by autonomously completing assigned tasks, reducing labor costs while the server manages high-level coordination
Solution Approach 2:
The server implements a universal task allocation framework that can distribute various types of tasks (picking, transporting, loading) to multiple robots with different capabilities. This multi-functional system handles diverse order types and warehouse scenarios through a single coordinated platform, managing complexity through standardized interfaces
4Ease of operation
If worker movement is limited through optimized robot paths, then worker fatigue decreases, but loss of time increases due to constrained movement options
Solution Approach 1:
The warehouse is divided into multiple zones with dedicated picking stations and loading boxes for each zone. Robots are assigned to specific zones and follow predetermined paths within those zones, limiting their movement to essential routes only. This spatial segmentation reduces unnecessary worker movement and fatigue while maintaining efficient operation within each zone
Solution Approach 2:
The system implements continuous automated robot operation along optimized paths, eliminating idle movement and positioning time. Robots continuously perform useful actions (picking, transporting, loading) along predetermined efficient routes, maximizing productive time while minimizing non-essential movement that would contribute to fatigue
Data Source
AI summary
Provided is a method of operating a server. The method includes generating a sequence for a plurality of orders based on shipments included in each of the plurality of orders, acquiring a plurality of groups including one or more consecutive orders among the plurality of sorted orders according to the generated sequence, allocating the plurality of groups to a plurality of robots such that the groups and robots are matched one-to-one, and allocating tasks to the plurality of robots based on orders included in each group.