Magnetic Tool Changer Control for Adaptive Robotic Picking
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Solution Overview
Problem
Robotic manipulators face challenges in efficiently processing collections of objects with varying types, as existing systems struggle to determine the optimal order of operations and tool changes based on object characteristics, leading to inefficiencies and potential damage.
Innovation Solution
A method and system that involves collecting information on object characteristics, determining an initial optimal order for robotic arm operations, updating this order based on additional information after each pick, and using a learning algorithm to adjust tool usage dynamically, incorporating features like magnetically engaged tool changers and compliant elements for adaptable gripping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed tool order is used for processing objects, then the control system is simple, but the processing efficiency decreases when object types vary
Solution Approach 1:
The patent implements dynamic tool order determination by collecting object characteristics during processing and updating the optimal tool sequence in real-time. The system transitions from a static pre-defined tool order to a dynamic adaptive sequence that responds to actual object types encountered, thereby improving processing efficiency without excessive complexity increase.
Solution Approach 2:
The system collects information about object characteristics (type, dimensions, packaging) during the processing sequence and uses this feedback to update the optimal tool order. This closed-loop approach allows the system to adapt to varying object types and optimize tool selection based on actual processing needs rather than relying on predetermined sequences.
2Adaptability or versatility
If tool changes are made frequently to handle different object types, then processing adaptability improves, but downtime increases
Solution Approach 1:
The system determines the optimal tool order in advance by collecting object characteristics and performing optimization calculations before actual processing begins. This preliminary planning allows the robotic arm to execute a pre-optimized tool sequence, minimizing the need for frequent unplanned tool changes and reducing downtime while maintaining adaptability to different object types.
Solution Approach 2:
The system identifies and prioritizes processing of objects that can be handled by currently attached tools, skipping tool change operations for these objects. By rushing through processing with existing tools whenever possible and only changing tools when absolutely necessary, the system reduces downtime while maintaining processing adaptability.
3Speed
If the robotic arm processes objects without updating tool order, then operational speed is high, but object damage risk increases
Solution Approach 1:
The system automatically collects object characteristics, determines optimal tool sequences, and updates processing orders without requiring external intervention. This self-service capability allows the system to adapt to different object types in real-time, selecting appropriate tools to minimize damage risk while maintaining operational speed through automated decision-making.
4Manufacturing precision
If comprehensive object information is collected continuously, then processing precision improves, but system complexity increases
Solution Approach 1:
The system collects only the necessary object characteristics (type, dimensions, packaging) required for optimal tool selection rather than comprehensive continuous monitoring of all possible parameters. This partial information collection approach achieves sufficient processing precision for tool selection while avoiding the excessive complexity of complete continuous monitoring systems.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables efficient and adaptive processing of diverse objects by optimizing tool usage and reducing downtime, improving the robotic arm's ability to handle different types and shapes while minimizing damage and increasing operational efficiency.
Implementation Method 1
The tool changer includes a proximal magnet embedded within the proximal engagement plate and the tool includes a distal magnet embedded within the distal engagement plate
Data Source
AI summary
A tool changer at a distal end of a robotic arm may include a proximal engagement plate and a tool may include a distal engagement plate magnetically engaged with the proximal engagement plate. The tool changer may be configured to magnetically engage and disengage with a variety of tools as different tools are needed for operations being performed by the robotic arm. Decisions regarding which tools to couple to the tool changer may be made on-the-fly and based on changing circumstances as the robotic arm is used to operate on objects.