Autonomous Mixed-Pallet Picking With Item Identification Robotics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Automating order picking from mixed inventory storage locations is challenging due to the presence of multiple differing types of items, which increases labor costs and reduces efficiency in warehouse operations.
Innovation Solution
A mobile articulating robot system with an end effector and item type identification system that autonomously locates and picks items from mixed storage locations, using barcode scanners, RFID tags, or vision systems to identify items and place them into temporary storage, thereby facilitating efficient order fulfillment and inventory management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If items are homogenously stored in fixed locations on static shelving, then picking accuracy is improved, but storage space utilization deteriorates
Solution Approach 1:
The system transitions from static shelving with fixed homogeneous storage to dynamic mobile shelving units that can be repositioned. The mobile articulating robot dynamically navigates between these units, enabling the storage system to adapt its configuration based on picking requirements while maintaining high space utilization through mixed SKU storage.
2Adaptability or versatility
If a human order picker operates a forklift to pick items one at a time, then flexibility in handling mixed items is improved, but productivity deteriorates
Solution Approach 1:
The mobile articulating robot autonomously performs the complete picking process without human intervention. It independently navigates to mobile shelving units, identifies target items using vision systems, manipulates items with specialized end effectors, and transports them to designated locations. This automation maintains flexibility in handling mixed SKUs while dramatically increasing productivity through continuous autonomous operation.
3Volume of stationary object
If multiple types of items are stored in a single inventory storage location, then storage space utilization is improved, but device complexity deteriorates
Solution Approach 1:
The mobile articulating robot is designed as a universal platform capable of handling multiple different item types through interchangeable end effectors and advanced vision systems. This multi-functionality allows the system to work with mixed SKU storage locations without requiring separate specialized equipment for each item type, thereby managing complexity while enabling high storage utilization.
Solution Approach 2:
The system replaces manual human operations with automated robotic systems equipped with computer vision and intelligent control. The vision system identifies items based on visual characteristics, the control system plans pick paths and sequences, and the end effectors manipulate diverse item types mechanically. This substitution of human cognition and manipulation with automated systems manages the complexity of handling mixed items efficiently.
4Productivity
If robots are used to automate order picking of homogenously-stored items, then productivity is improved, but adaptability deteriorates
Solution Approach 1:
The system employs mobile articulating robots with dynamic navigation capabilities that can adapt to different storage configurations. The robots use vision systems to identify and locate items in mixed SKU environments, and interchangeable end effectors that can be selected based on item characteristics. This dynamic adaptability enables the same robotic platform to handle both homogeneous and mixed storage scenarios with high productivity.
Data Source
AI summary
Robotic systems can autonomously pick a particular desired item from a mixed inventory storage location that includes multiple differing types of items. The autonomous robotic system can thereby facilitate order fulfillment and inventory management processes in an efficient manner. In particular, the systems and methods described herein can greatly reduce the amount of time required for a human worker to pick orders. The systems described take over many of the tasks related to picking items. Accordingly, the efficiency of order picking processes, as measured by the number of line items picked per human labor hour for example, is greatly enhanced.