Stock Pickup Queue Layout for Flexible Robot Shelf Routing
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Solution Overview
Problem
Existing robot-based goods-to-person systems face limitations in queuing capacity, fixed rotation zones causing bottlenecks, and inefficient space utilization, leading to reduced picking efficiency and increased waiting times for operators.
Innovation Solution
A picking zone design with multiple U-shaped or parallel picking passages and points, allowing flexible queuing and rotation strategies, including queue-jump mechanisms and symmetrical station layouts, along with dynamic route adjustments and multi-operation points to enhance efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed queuing zone is used with limited capacity, then the zone structure is simple, but robots that cannot be accommodated obstruct routes and cause deadlocks
Solution Approach 1:
The patent implements dynamic queuing zones that can expand and contract based on real-time robot flow demands. Instead of fixed boundaries, the system uses virtual fencing and dynamic route planning to allow queuing zones to adapt their capacity and location, preventing route obstructions while maintaining simple physical infrastructure.
Solution Approach 2:
The system transitions from two-dimensional fixed zone planning to three-dimensional spatial-temporal management by implementing multi-layer routing and dynamic zone allocation. Robots can switch between different queuing zones and routes based on real-time conditions, effectively adding a temporal dimension to zone utilization.
2Productivity
If a fixed side-turning zone is used, then the rotation operation is standardized, but it becomes a bottleneck when multiple robots need side-turning operations
Solution Approach 1:
The patent divides the side-turning operation into multiple distributed turning points throughout the workspace rather than concentrating all rotations in a single fixed zone. Each robot can perform side-turning operations at the nearest suitable location, eliminating the bottleneck effect of centralized rotation zones.
Solution Approach 2:
The system performs preliminary route planning that anticipates side-turning needs before robots arrive at concentrated zones. By pre-positioning robots and planning routes that distribute turning operations, the system reduces waiting time and prevents bottleneck formation.
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 patent implements dynamic path planning that adapts entry trajectories to available space between shelves. Instead of fixed arc paths requiring uniform margins, the system calculates optimal entry angles and paths in real-time based on actual shelf spacing, allowing smoother rotations in tighter spaces when possible.
Solution Approach 2:
The system varies path parameters (radius, angle, speed) based on real-time spatial conditions. When shelf margins are limited, the system adjusts entry parameters to accommodate tighter spaces while maintaining operational smoothness, rather than requiring fixed large margins for all operations.
4Device complexity
If one picking point corresponds to one picking person, then the assignment is simple, but idle time occurs when persons wait for robots
Solution Approach 1:
The patent segments the picking workforce into multiple teams or zones, each responsible for specific picking points. This allows parallel robot assignments to multiple picking points simultaneously, reducing idle waiting time while maintaining manageable assignment complexity through hierarchical coordination.
Solution Approach 2:
The system introduces an intelligent task allocation intermediary that dynamically matches robots with picking persons based on real-time status. This intermediary layer coordinates multiple robots and persons efficiently, reducing idle time without significantly increasing perceived complexity for individual operators.
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.