Robotic Shuttle Transfer for High-Throughput Inventory Picking
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Solution Overview
Problem
Current robotic systems are inefficient in picking and sorting inventory with varying sizes, dimensions, shapes, weights, and stiffness, often requiring human intervention and being labor-intensive due to the inability to reliably grasp and sort diverse items.
Innovation Solution
A robotic system comprising a robot with a picking arm and a shuttle device, where the picking arm can operate in autonomous or teleoperator modes, using a machine learning grasp pose prediction algorithm to improve grasping accuracy, and a shuttle platform to efficiently transfer items between locations, reducing the need for human intervention and increasing sorting throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single robot performs the entire sorting process (recognizing, grasping, verifying, and placing items), then the system can handle diverse inventory items, but the process becomes time-consuming and requires long travel distances between locations
Solution Approach 1:
The sorting system is divided into separate functional components: a robot station for item recognition and grasping, and a separate shuttle mechanism for transport and placement. This segmentation allows each component to specialize in specific tasks, improving overall efficiency while maintaining the ability to handle diverse items.
Solution Approach 2:
A shuttle platform acts as an intermediary between the robot's picking location and the sorting locations. The robot places items on the shuttle, which then transports them to the appropriate destination, eliminating the need for the robot to travel long distances for each item placement.
2Adaptability or versatility
If robots are equipped with advanced grasping mechanisms to handle items of varying sizes and properties, then the system can autonomously sort diverse inventory, but the device complexity and cost increase significantly
Solution Approach 1:
The robot uses a universal grasping mechanism that can handle various item types through adaptive control algorithms. Rather than having specialized grippers for each item type, the system uses a single versatile end-effector with machine learning-based pose prediction to adapt to different geometries and properties.
Solution Approach 2:
Complex mechanical grasping mechanisms are replaced with machine learning-based grasp pose prediction algorithms. The system uses computational methods to determine optimal grasping strategies, substituting sophisticated hardware with intelligent software control.
3Productivity
If items are transferred from the picking arm to distant sorting locations, then the sorting process can be completed, but items may be unintentionally dropped requiring human intervention
Solution Approach 1:
The shuttle platform serves as a stable intermediary for item transfer. Items are placed on the shuttle's platform, which provides a secure holding surface during transport to sorting locations, eliminating the risk of items being dropped during transit.
Solution Approach 2:
The system designs the transfer mechanism to prevent dropping before it occurs. The shuttle platform is positioned and sized to ensure stable item placement, and the transfer process is controlled to minimize the risk of items being unintentionally released during transport.
Data Source
AI summary
A robotic system includes a robot having a picking arm to grasp an inventory item and a shuttle. The shuttle includes a platform adapted to receive the inventory item from the picking arm of the robot. The platform is moveable between a pick-up location located substantially adjacent to the robot and an end location spaced a distance apart from the pick-up location. The system improves efficiency as transportation of the item from the pick-up location to the end location is divided between the robot and the shuttle.