Spray Painting Robot Trajectory Optimization
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Solution Overview
Problem
Existing systems for controlling painting robots struggle to optimize painting trajectories for objects with complex or variable surface geometries, particularly in low-volume production, focusing mainly on painting quality rather than energy consumption and process time.
Innovation Solution
A system comprising a controller connected to a 3D scanner and a painting robot, which generates a point cloud model of the object surface and determines an optimized painting trajectory by applying an optimization algorithm to minimize energy consumption and process time costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-programmed software instructions or CAD models are used to define painting trajectories, then painting quality can be maintained, but the system becomes impractical for low-volume production and objects with variable surface geometries
Solution Approach 1:
The system creates a digital copy (point cloud model) of the object surface through 3D scanning, which serves as the basis for automated trajectory generation. This eliminates the need for manual programming or CAD modeling for each new geometry, enabling the system to adapt to variable surfaces without increasing operational complexity.
Solution Approach 2:
The system performs self-service by automatically generating painting trajectories from the scanned point cloud data without requiring external programming or CAD intervention. The automated trajectory generation algorithm processes the geometric data and produces optimal paths independently, making the system self-sufficient for handling diverse geometries.
2Use of energy by moving object
If traditional painting trajectories are used, then painting quality is maintained, but energy consumption and process time are not optimized
Solution Approach 1:
The system dynamically adjusts trajectory parameters (speed, direction, spacing) based on the object geometry and optimization objectives. By varying these parameters along different segments of the painting path, the system achieves both energy efficiency and consistent paint application, resolving the contradiction between energy consumption and coating quality.
Solution Approach 2:
The painting trajectory is made dynamic rather than static, allowing real-time adjustments to speed and path based on local geometric features. This dynamic approach enables the robot to optimize energy consumption in different regions while maintaining precise paint application standards throughout the entire surface.
3Productivity
If painting robot moves at high speed to reduce process time, then productivity increases, but energy consumption increases and painting quality may deteriorate
Solution Approach 1:
The painting process uses periodic variation in speed along the trajectory, with faster movement in less critical areas and slower movement where precision is needed. This periodic speed modulation allows the system to maintain high overall productivity while managing energy consumption and ensuring quality where required.
Solution Approach 2:
Different segments of the painting trajectory are assigned different speed characteristics based on local requirements. Critical areas receive slower, more precise painting with higher energy input, while non-critical areas use faster, lower-energy passes. This local differentiation resolves the contradiction between overall productivity and localized energy/quality requirements.
Data Source
AI summary
A system, and a related method and a related computer-program product are provided for controlling a 3D scanner and a painting robot to spray paint an object having an object surface are provided. The painting robot has an end effector adapted to hold or comprising a paint spray gun. The system includes a processor and a memory that stores instructions executable the processor to: control the 3D scanner to scan the object to generate the point cloud model of the object surface; based on the point cloud model of the object surface, determine an optimized painting trajectory defined by slices of the point cloud model; and based on the optimized painting trajectory, control actuation of the painting robot to paint the object surface. Determining the optimized painting trajectory comprises applying an optimization algorithm to minimize a cost function defined by an energy consumption cost and/or a process time cost.