Robot Assembly Event Detection Using Sensor Training Data
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Solution Overview
Problem
The definition and parameterization of events for robot and capture means arrangements in automation systems are labor-intensive and require significant effort, making it difficult to efficiently control these systems, especially in partially automated operations.
Innovation Solution
A method that uses sequences of ordinate data assigned to abscissa points based on training data to identify event points and determine event criteria, allowing for automated improvement of event detection and parameterization, including the use of sensors and data processing to capture forces, positions, and time derivatives, and varying training data to optimize event criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual definition and parameterization of events is used in robot and capture means arrangements, then event detection can be achieved, but the work required for generating or modifying programs becomes excessively large and labor-intensive
Solution Approach 1:
The system performs self-learning by automatically analyzing sensor data sequences to identify event points and determine event criteria. The robot arrangement autonomously generates event definitions without requiring manual programming, thereby reducing labor-intensive work while maintaining reliable event detection through data-driven parameter optimization
Solution Approach 2:
The system collects and analyzes training data sequences beforehand to pre-determine event criteria and parameters. By performing preliminary analysis of sensor data during training operations, the system prepares event detection parameters in advance, which significantly reduces the time and effort required for subsequent program generation and modification
2Measurement precision
If multiple sensors and capture means are used to capture forces, positions, and time derivatives, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system uses a multi-functional sensor arrangement where the same sensors (force sensors, position sensors, torque sensors) serve multiple purposes: capturing forces, positions, and time derivatives simultaneously. This universal approach allows comprehensive measurement precision improvement without proportionally increasing device complexity, as one sensor system performs multiple measurement functions
Solution Approach 2:
The system combines multiple measurement functions (force capture, position capture, time derivative calculation) into a unified data processing framework. By merging these functions into a single automated analysis system that processes sequences of ordinate data, the complexity of managing multiple sensors is reduced while maintaining high measurement precision
Data Source
AI summary
The invention relates to a method for controlling an automation assembly which has a robot assembly with at least one robot (10) and a detection means assembly with at least one detection means (21-23), said method having the following at least partly automated steps: providing (S10) a first sequence of first ordinate data (q1, q2, dq2/dt, τ1, τ2) assigned to successive first abscissa points (t) on the basis of first training data (q1, q2, τ1, X2); identifying (S20) a first event point (tE) within the first abscissa points of the first sequence; and determining (S30) a first event criterion on the basis of the first sequence and the first event point.