Coating Robot Configuration Sequencing for Collision-Free Path Execution
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Solution Overview
Problem
Existing methods for programming the movement path of multi-axis painting robots are complex, particularly for robots with redundancy, requiring manual input of multiple information items and are not reproducible, leading to inefficiencies and quality dependence on programmer experience.
Innovation Solution
An optimization method that calculates and selects the best robot configurations for each path point based on quality values, considering spatial position, orientation, and interference contours to avoid collisions and minimize movement, using a computer unit to determine an optimized sequence of robot configurations for efficient path execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual programming methods are used for multi-axis painting robots, then the robot path can be programmed, but the programming process becomes very complex and requires extensive manual input of information items
Solution Approach 1:
The system enables automatic programming by allowing the robot to learn and record its own movement paths through sensors and control units that automatically generate program data from observed movements, eliminating the need for complex manual programming input
Solution Approach 2:
Manual programming operations are replaced by an automated control system that uses sensors, processing units, and algorithms to automatically determine and store robot path information, substituting human operator input with automated electronic systems
2Adaptability or versatility
If manual programming is used for robots with redundancy, then the robot path can be defined, but adaptation becomes necessary whenever the robot position changes
Solution Approach 1:
The system continuously monitors robot position and path execution through sensors and control units, automatically detecting position changes and adjusting the program data in real-time to maintain accurate path following without requiring manual reprogramming
Solution Approach 2:
The programming system transitions from static manual programming to dynamic automatic adaptation, where the robot path program is continuously updated based on real-time position feedback and changing operational conditions
3Reliability
If manual programming is used, then the robot path can be programmed, but reproducible programming is not possible since quality depends on programmer experience and skill
Solution Approach 1:
The robot system automatically programs itself by recording its own movement data and path information through sensors and control units, eliminating dependence on individual programmer skill levels and ensuring consistent programming quality across different operators
Solution Approach 2:
The system creates accurate digital copies of the robot's actual movement paths through sensors and data recording, storing precise path information that can be consistently reproduced without variation, replacing subjective manual programming with objective measured data
4Manufacturing precision
If complex programming methods are used, then the robot path can be defined with precise positioning, but the programming process is not efficient and requires extensive manual input
Solution Approach 1:
Manual programming operations are replaced by automated electronic systems that use sensors, processing units, and algorithms to automatically determine and store precise robot path information, maintaining positioning accuracy while dramatically improving programming efficiency
Solution Approach 2:
The system pre-calculates and stores optimal path points and robot configurations before execution, using processing units to determine precise positioning data in advance, eliminating the need for complex manual programming while ensuring precision
Data Source
AI summary
The disclosure relates to an optimisation method for calculating an optimised movement path of a coating robot (1), including the following steps:defining consecutive path points of the movement path using path point data, wherein the path point data defines the spatial position and orientation of the application device (7) at each path point; calculating possible robot configurations for the individual path points of the movement path, wherein each robot configuration includes all axial positions of all robot axes (A1-A7) and at least some of the path points can be reached optionally via multiple different robot configurations;calculating a path point-related and preferable also sequence-related quality value individually for the different possible robot configurations of the individual path points, such that each robot configuration is assigned a respective quality value; and—selecting one of the possible robot configurations for the individual path points according to the quality value of the different possible robot configurations. The disclosure also comprises a corresponding coating system.