Multi-Robot Picking Assignment with Priority Location Groups
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Solution Overview
Problem
Conventional logistics warehouses rely heavily on human labor for shipment delivery, despite the availability of remote information systems, as the assignment of picking tasks to robots is not efficiently managed.
Innovation Solution
A server system that assigns picking tasks to multiple robots by generating picking rounds, identifying idle robots, and assigning them to location groups based on priority, proximity, and shipment item quantities, ensuring efficient task distribution and movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple robots are deployed for picking tasks, then productivity increases, but device complexity increases due to task assignment management
Solution Approach 1:
The system segments the warehouse into multiple location groups and divides picking tasks into discrete picking units that can be independently assigned to different robots. This segmentation allows parallel processing of multiple tasks by multiple robots while maintaining manageable complexity through modular task units.
Solution Approach 2:
The task assignment system dynamically adjusts robot assignments based on real-time robot states (idle, busy, location) and task priorities. The server continuously monitors and reassigns tasks to optimize productivity while adapting to changing conditions, preventing system overload and managing complexity through flexible, state-based decision making.
2Productivity
If manual picking is used, then device complexity is low, but productivity and efficiency deteriorate
Solution Approach 1:
The system enables semi-autonomous operation where robots autonomously navigate to locations and execute picking tasks, while the server provides high-level task assignment and coordination. This self-service approach allows robots to perform complex picking operations with minimal human intervention, significantly improving productivity while maintaining reasonable automation complexity.
3Productivity
If robots are assigned to multiple location groups, then task completion efficiency improves, but loss of time increases due to movement between locations
Solution Approach 1:
The system performs preliminary actions by pre-calculating optimal task assignments and routing before robots execute tasks. The server anticipates robot movements and proactively assigns tasks that minimize travel time, such as assigning nearby location groups to robots already in proximity, thereby reducing overall movement time while maintaining high task completion efficiency.
Data Source
AI summary
Disclosed is a method of controlling at least one server configured to assign a task to a plurality of robots assisting to a worker. The control method includes generating one or more picking rounds including a plurality of orders with respect to the same shipment, identifying at least one robot corresponding to an idle state as a target robot, selecting a picking round with the highest priority among the one or more picking rounds in progress, assigning the target robot to the selected picking round, selecting a location group matching a picking unit in a waiting state among a plurality of location groups in which locations at which shipments related to the selected picking round are stored are grouped, and assigning the target robot to the selected location group and assigning a picking task to the target robot according to a picking unit matching the selected location group.