Robotic Control Planning From Assembly Manual Images
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Solution Overview
Problem
Traditional robotic movement planning is labor-intensive, time-consuming, and error-prone, requiring manual programming that is not easily adaptable across different robotic operating environments, especially when multiple robots need to coordinate movements to avoid collisions in complex tasks.
Innovation Solution
A system that uses machine learning models to generate robotic control plans from instruction manuals, allowing for automatic planning of robotic movements in new environments by processing images of assembly tasks and generating instruction data for robotic components to assemble complex items efficiently and accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual programming is used to dictate robotic movements, then the robotic plan can be precisely controlled to accomplish tasks, but the programming becomes tedious, time-consuming, and error-prone
Solution Approach 1:
The patent replaces manual mechanical programming with an automated machine learning system that generates robotic control plans. The system uses trained ML models to automatically translate task descriptions into executable robotic movement sequences, eliminating the need for tedious manual programming while maintaining precise control over robotic actions.
Solution Approach 2:
The robotic system performs self-programming through the machine learning model, which automatically generates control plans based on task requirements without human intervention. The system learns from training data and autonomously creates executable plans, making the programming process self-service rather than relying on manual programming efforts.
2Manufacturing precision
If manual programming is used for one robotic operating environment, then the plan can be optimized for that specific environment, but the plan cannot be easily adapted to other environments with different physical dimensions
Solution Approach 1:
The machine learning model serves as a universal planning system that can generate control plans for multiple different robotic operating environments. By training the model on diverse environment data, it learns to adapt to various physical dimensions and configurations, allowing a single system to handle multiple environments without requiring separate manual programming for each.
Solution Approach 2:
The system adapts to different environments by dynamically adjusting parameters such as physical dimensions, robot positions, and movement constraints. The machine learning model takes environment-specific parameters as input and generates optimized control plans tailored to each environment's unique characteristics, enabling seamless adaptation across diverse settings.
3Productivity
If multiple robotic components are used to perform tasks simultaneously, then productivity increases, but the search space in 6D coordinate system becomes very large and cannot be searched exhaustively in reasonable time
Solution Approach 1:
The patent replaces exhaustive search methods with a machine learning-based planning system that directly generates coordinated movement plans for multiple robots. Instead of searching through the vast 6D coordinate space, the trained ML model predicts optimal coordination strategies based on task requirements, dramatically reducing computation time while maintaining collision-free operation.
Solution Approach 2:
The system performs preliminary learning during the training phase, where the machine learning model learns from simulated multi-robot scenarios and collision patterns. This preliminary action allows the model to quickly generate coordinated plans during execution without needing to search the coordination space in real-time, enabling efficient multi-robot operation.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a robotic control plan. One of the methods includes obtaining, from a user device, image data depicting an instruction manual for assembling a plurality of assembly components; processing the image data using a machine learning model to generate instruction data representing a sequence of instructions for assembling the plurality of assembly components, wherein the machine learning model has been configured through training to process images depicting instruction manuals and to generate instruction data characterizing sequences of instructions identified in the instruction manuals; processing the instruction data to generate a robotic control plan to be executed by one or more robotic components for assembling the plurality of assembly components; and providing the robotic control plan to a robotic control system for executing the robotic control plan using the one or more robotic components.