AI Picking Vehicle Placement for Aligned, High-Occupancy Racks
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Solution Overview
Problem
Manual picking of goods in logistics leads to labor shortages and inefficiencies, with goods often misaligned or protruding from racks, making it difficult to maintain a high occupancy rate.
Innovation Solution
A picking method and system utilizing an AI-driven picking vehicle with movable forks and a pushing component to optimize the placement of goods based on volume and weight data, ensuring accurate stacking and maximizing rack occupancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual picking is used, then labor flexibility is maintained, but labor shortage occurs and picking efficiency decreases
Solution Approach 1:
The patent replaces manual mechanical picking operations with an automated picking vehicle system that uses sensors, controllers, and mechanical components to automatically pick, transport, and place goods. The system substitutes human labor with automated machinery including scanning devices, movable bases, and positioning systems.
Solution Approach 2:
The picking vehicle is equipped with autonomous navigation and identification capabilities, allowing it to independently locate racks, scan product codes, determine optimal placement positions, and execute picking operations without continuous human intervention. The system performs self-guided operations based on stored product information and rack configurations.
2Manufacturing precision
If manual stacking is used, then operation simplicity is maintained, but goods misalignment and rack occupancy issues occur
Solution Approach 1:
The system uses scanning devices to read product codes and obtain real-time information about goods, then processes this data to determine optimal placement positions. The controller receives feedback from sensors and product information to automatically adjust placement decisions, ensuring precise positioning and high rack occupancy rates.
Solution Approach 2:
The system pre-stores product information including dimensions, weight, and placement requirements in a database. Before actual picking operations, the system retrieves this information to pre-calculate optimal placement positions, allowing for precise positioning without complex real-time calculations during the picking process.
3Adaptability or versatility
If vacuum chunk is used for picking, then picking automation is achieved, but goods of various sizes cannot be accommodated
Solution Approach 1:
The picking vehicle uses movable bases and adjustable mechanical components that can dynamically adapt to different product sizes and shapes. The system includes movable forks and positioning mechanisms that can be adjusted based on the dimensions of the goods being handled, providing versatility across various product types.
Solution Approach 2:
The picking vehicle is designed with universal handling capabilities that can accommodate different types of goods including boxes, bags, and irregularly shaped items. The system uses multiple sensing devices and adjustable mechanical components to handle various product forms, replacing the size-limited vacuum chunk approach.
Data Source
AI summary
A picking method, a picking vehicle and a picking system. The picking method includes: obtaining a current volume data and a current weight data of a box; generating a current arranging position data of the box based on the current volume data, the current weight data and an arranging position model of an artificial intelligence model. The arranging position model includes multiple arranging date each including a predetermined volume data, a predetermined weight data and a predetermined arranging position data that are corresponding to one another; and moving the box to a target arranging position via a picking vehicle based on the current arranging position data.