Shelf Queue Layout for Deadlock-Free Robot Picking Stations
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Solution Overview
Problem
The existing robot-based goods-to-person systems in logistics automation face inefficiencies due to limited queuing capacity, route deadlocks, and bottlenecks in the side-turning zone, which hinder overall sorting efficiency and space utilization in warehouses.
Innovation Solution
A shelf management system that includes a mobile robot for transporting shelves, a server for real-time queuing and routing optimization, and flexible queuing zone layouts such as dual-station symmetrical, parallel, and multi-operation point layouts to dynamically allocate resources and reduce bottlenecks, allowing for efficient shelf rotation and operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed queuing zone is used, then the system structure is simple, but the number of robots that can be accommodated is limited causing route obstruction
Solution Approach 1:
The fixed queuing zone is divided into multiple virtual queuing areas that can be dynamically allocated. Instead of having one large fixed zone, the system segments the space into multiple smaller queuing regions that can be assigned to different stations based on real-time需求的, thereby increasing the overall robot accommodation capacity without adding physical infrastructure.
Solution Approach 2:
The system transitions from a static fixed queuing zone to a dynamic virtual queuing mechanism. The server dynamically assigns virtual queuing areas to robots based on real-time station availability and robot positions, allowing the queuing capacity to adapt flexibly to changing system conditions and accommodate more robots simultaneously.
2Ease of operation
If a fixed side-turning zone is used, then the rotation operation is straightforward, but it becomes a bottleneck when multiple robots need side-turning operations
Solution Approach 1:
The single fixed side-turning zone is segmented into multiple virtual side-turning areas distributed across different locations in the warehouse. Each area can independently perform side-turning operations, allowing multiple robots to execute rotation operations simultaneously without conflicting for a single bottleneck resource.
Solution Approach 2:
The system replaces the static fixed side-turning zone with dynamic virtual side-turning areas that can be allocated to different robots based on their positions and task requirements. This dynamic allocation eliminates the bottleneck by allowing flexible, parallel side-turning operations across multiple locations.
3Ease of operation
If an arc path is used to enter the rotation zone, then the rotation operation is smooth, but a large margin between shelves is required reducing space utilization
Solution Approach 1:
The rotation operation is segmented into multiple smaller rotational steps performed at different virtual side-turning areas rather than requiring a single large arc path. This segmentation allows robots to perform incremental rotations in tighter spaces, reducing the margin requirements between shelves while maintaining operational smoothness.
Solution Approach 2:
The system replaces the fixed arc path with dynamic rotation paths that can be adjusted based on the robot's position and the available space. The server calculates optimal rotation trajectories that minimize space requirements while ensuring smooth rotation operations, adapting to the actual warehouse layout and shelf arrangements.
4Device complexity
If a fixed queuing route is used, then the route planning is simple, but route deadlocks are likely to occur
Solution Approach 1:
The system replaces fixed queuing routes with dynamic virtual queuing areas and adaptive route planning. The server continuously monitors robot positions and dynamically adjusts queuing area assignments and route recommendations to prevent deadlocks, while maintaining simple operation through centralized control.
Solution Approach 2:
The system implements feedback mechanisms where the server continuously monitors the positions of robots in virtual queuing areas and adjusts route planning based on real-time system state. This feedback loop prevents route deadlocks by detecting potential conflicts early and redirecting robots to alternative paths or queuing areas before deadlocks occur.
Data Source
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AI summary
The present application discloses a shelf management method and system, a pickup area, and a stock pickup system. The shelf management method comprises: predicting whether there is space in the station queue area of a station; when it is predicted that there is space in the station queue area, selecting a shelf from among the shelves allocated to the station and have not yet been transported, and controlling a moving robot to transport the selected shelf; after the moving robot is engaged with the selected shelf, re-predicting whether there is space in the station queue areas of all the stations that need the selected shelf; when it is predicted that there is space, controlling the moving robot to transport the selected shelf to the station queue area that has been predicted to have space, and when the moving robot has transported the selected shelf to the pre-set area around the station queue area that has been predicted to have space, determining whether there is newly available space in the station queue area that has been predicted to have space; when it is determined that there is newly available space in the station queue area that has been predicted to have space, controlling the moving robot to enter the station queue area that has been predicted to have space.