Fixtureless Robotic Part Placement Using Vision-Based Virtual Datums
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Solution Overview
Problem
The automotive and manufacturing industries face significant costs and obsolescence issues with dedicated hardware fixtures for sheet metal welding, as well as challenges in handling and joining differently shaped and sized parts, which traditional fixtures struggle to accommodate efficiently.
Innovation Solution
A reconfigurable, fixtureless manufacturing system utilizing learning AI software and material handling robots with machine vision systems to create virtual datums, allowing for the secure handling and joining of various parts without physical fixtures, enabling flexible and adaptive assembly processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If dedicated hardware fixtures are used to secure and locate sheet metal parts for welding, then manufacturing precision and stability are improved, but device complexity and manufacturing costs increase significantly
Solution Approach 1:
The patent replaces physical fixtures with a robotic system equipped with sensors and control algorithms. The robot uses vision systems and tactile sensors to detect part positions and automatically adjusts its gripping and placement actions, eliminating the need for complex mechanical fixtures while maintaining positioning precision.
Solution Approach 2:
The system enables parts to self-locate through their own geometric features. The robotic system detects natural datums on the parts themselves and uses these self-provided reference points for positioning, rather than requiring external fixtures to impose positioning constraints.
2Manufacturing precision
If dedicated hardware fixtures are manufactured for each subassembly, then manufacturing precision is improved, but loss of time and productivity decrease due to fixture design and manufacturing cycles
Solution Approach 1:
The system transitions from static fixtures to a dynamic robotic system that can adapt its positioning strategy in real-time. The robot can adjust its approach based on detected part variations and automatically recalibrate positions, eliminating the time required to manufacture and reconfigure physical fixtures for different assemblies.
Solution Approach 2:
The robotic system changes its operational parameters (gripping forces, approach vectors, positioning offsets) based on real-time sensor data and part characteristics. This allows the same physical system to handle different subassemblies with high precision without requiring physical reconfiguration or new fixtures.
3Adaptability or versatility
If flexible fixture systems are designed to accommodate minor changes, then adaptability is improved, but device complexity and manufacturing costs increase
Solution Approach 1:
The robotic system serves multiple functions: positioning, gripping, welding, and inspection. This single multi-functional system replaces multiple specialized fixtures, achieving versatility without the cumulative complexity of designing and manufacturing various flexible fixture systems for different operations.
Solution Approach 2:
The patent replaces mechanical fixture flexibility with software-based adaptability. The control system uses algorithms to adjust positioning and gripping strategies dynamically, providing the flexibility that would otherwise require complex mechanical adjustments or reconfigurable fixtures.
4Ease of operation
If traditional fixtures are used for differently shaped and sized parts, then ease of operation is maintained, but adaptability and productivity decrease due to fixture obsolescence with product changes
Solution Approach 1:
The robotic system dynamically adapts its gripping and handling strategies based on real-time detection of part geometry. The robot can adjust its approach vectors, gripping forces, and positioning offsets for each part configuration, maintaining ease of operation across different part shapes and sizes without requiring physical reconfiguration of fixtures.
Solution Approach 2:
The system changes its operational parameters (gripping points, approach angles, positioning offsets) based on detected part characteristics. This allows the same robotic system to handle diverse part geometries with the same ease as traditional fixtures handled their designated parts, while maintaining full adaptability to product changes.
Data Source
AI summary
Systems and methods for learning software assisted, fixtureless object pickup and placement include a machine vision system to scan a part for pickup to measure target points at said part, retrieves weighted desired target points for the part from a controller, and performs an iterative analysis to determine a best fit solution for moving the part within a predetermined range of the desired target points. The best fit solution being determined by fitting vectors between each of the desired target points and the measured target points to develop a solution set. The best fit solution having the overall shortest length when said vectors are summed while prioritizing relatively higher weighted desired target points.