Zero Teach Robotic Path Optimization via AR Simulation
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Solution Overview
Problem
The existing methods for programming industrial robots to follow a continuous path, such as applying a consistent bead of material, rely on trial-and-error approaches, leading to undesirable thickness variations due to velocity fluctuations at curves and corners, and are time-consuming and expensive.
Innovation Solution
A method that uses CAD data and process equipment characteristics to optimize robot motion commands, minimizing velocity fluctuations, and displays the results in an augmented reality system for verification and adjustment, allowing for precise path following without manual teaching cycles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional trial-and-error programming is used to teach robot paths, then the robot can eventually follow the path, but the process is time-consuming and expensive
Solution Approach 1:
The system performs preliminary optimization calculations using CAD data and process equipment characteristics models before actual robot execution. The optimization process pre-computes the ideal tool center point path and corresponding process parameters, eliminating the need for time-consuming trial-and-error teaching cycles during implementation
Solution Approach 2:
The system creates a virtual model of the process equipment characteristics and uses this digital copy to simulate and optimize the robot path. By working with the digital model rather than physical trial-and-error, the system achieves precise path planning without repeated physical adjustments
2Ease of operation
If the robot moves at constant speed along the path, then the motion is simple to control, but the bead thickness varies at curves and corners
Solution Approach 1:
The optimization process dynamically adjusts process parameters including tool center point position, velocity, and process equipment settings based on the specific geometry of the path. The system computes optimal parameter profiles that account for curves and corners, ensuring consistent bead thickness while maintaining smooth robot motion
Solution Approach 2:
The system transitions from static constant-speed control to dynamic speed variation along the path. The optimized velocity profile adjusts speed automatically based on path geometry, slowing down at curves and corners to maintain consistent material deposition while keeping the control system manageable through pre-computation
3Manufacturing precision
If the robot follows the prescribed path exactly, then the path accuracy is high, but velocity fluctuations occur causing inconsistent operation results
Solution Approach 1:
The optimization process simultaneously optimizes both position and velocity parameters along the path. Rather than treating velocity as a separate issue, the system computes coordinated position-velocity profiles that maintain path accuracy while ensuring smooth, consistent velocity that produces stable operation results
Solution Approach 2:
The system pre-computes the optimized velocity profile along with the path trajectory before execution. By calculating the complete motion profile in advance including velocity variations, the system eliminates velocity fluctuations during actual operation while maintaining exact path following
Data Source
AI summary
A method and system for programming a path-following robot to perform an operation along a continuous path while accounting for process equipment characteristics. The method eliminates the use of manual teaching cycles. In one example, a dispensing robot is programmed to apply a consistent bead of material, such as adhesive or sealant, along the continuous path. A computer-generated definition of the path, along with a model of dispensing equipment characteristics, are provided to an optimization routine. The optimization routine iteratively calculates robot tool center point path and velocity, and material flow, until an optimum solution is found. The optimized robot motion and dispensing equipment commands are then provided to an augmented reality (AR) system which allows a user to visualize and adjust the operation while viewing an AR simulation of dispensing system actions and a simulated material bead. Other examples include robotic welding or cutting along a continuous path.