Robot Localization Offset Correction via Digital Model
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Solution Overview
Problem
Existing robots used for industrial assembly and manufacturing operations face challenges with localization uncertainty, leading to increased costs, complexity, and latency due to the reliance on external and secondary sensor systems for accurate positioning, which can result in assembly task failures and higher maintenance costs.
Innovation Solution
The implementation of a robot manager system that determines and applies a localization offset to robot trajectories, allowing robots to perform assembly tasks without user intervention or secondary sensor systems by calculating correction factors based on actual and estimated poses, thereby reducing the need for additional sensors and simplifying the programming and maintenance of robots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If object recognition systems are used to guide robotic arms for assembly tasks, then positioning accuracy is improved, but system cost and complexity increase
Solution Approach 1:
The patent extracts and removes the object recognition system from the robotic assembly system. Instead of using complex external sensors and vision systems to recognize and locate objects, the invention uses a simplified approach where the robot follows pre-determined trajectories based on a digital model, eliminating the need for real-time object recognition and significantly reducing system complexity while maintaining positioning accuracy
Solution Approach 2:
The patent creates a digital copy or model of the assembly and its components, storing precise position information in a database. The robot uses this digital model to determine trajectories and positions without needing physical sensors to detect objects. The digital twin approach allows the system to achieve positioning accuracy through computational methods rather than complex sensing
2Measurement precision
If external sensor systems are added to improve robot localization, then positioning accuracy is improved, but device complexity and maintenance cost increase
Solution Approach 1:
The patent makes the robot self-sufficient for localization by using its own motion control system and pre-stored trajectory data. Instead of relying on external sensors to provide localization information, the robot uses its commanded positions and a digital model of the assembly to determine where it should be and how to get there, eliminating the need for complex external sensing infrastructure
Solution Approach 2:
The patent replaces mechanical and optical sensor systems with a computational approach. Instead of using cameras, lasers, or other external sensors to determine robot position, the system uses software-based trajectory calculation and digital modeling to achieve localization, substituting physical sensing with information processing
3Reliability
If secondary sensor systems are used for accurate positioning, then assembly task success rate is improved, but execution time and operational complexity increase
Solution Approach 1:
The patent performs all necessary positioning calculations and trajectory planning in advance. Before the robot begins assembly operations, the system stores pre-calculated trajectories and position information in a database based on the digital model. During execution, the robot simply follows these pre-determined paths without needing real-time sensor feedback or complex calculations, reducing execution time while maintaining task success rate
Solution Approach 2:
The patent implements a feedback mechanism where the robot's actual position is continuously compared with its commanded position from the digital model. When deviations are detected, the system calculates correction factors and adjusts subsequent trajectories to compensate, ensuring accurate assembly operations without requiring complex external sensor systems during execution
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed for robot control. An example apparatus includes a command generator to instruct a robot to move an end effector from a staging position to an estimated pre-task position to perform a first task based on a first pose of the robot, the first pose based on a model, adjust the robot to a first actual pre-task position to perform the first task when the robot is to move to a second actual pre-task position, the first actual pre-task position proximate the estimated pre-task position, and direct the robot to perform a second task based on a correction factor, the correction factor is to be determined by determining a second pose of the robot, the second pose corresponding to position information associated with a post-task position, and calculating the correction factor based on the first pose and the second pose.