Robot Trajectory Learning with Probe Sensor Feedback
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Solution Overview
Problem
Current methods for robot trajectory learning, especially for high precision tasks like welding, are cumbersome and time-consuming, particularly when dealing with complex shapes, as they require manual recording of single steps and struggle with translating these into continuous sequences due to robotic limitations in speed and acceleration.
Innovation Solution
A robot learning system equipped with a probe sensor that assists users in controlling the robot's tool center point by measuring distance and orientation relative to a complex surface, allowing continuous data logging and easier trajectory learning, even for complex paths, by maintaining the probe sensor in contact with the surface and using this data for real-time adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual recording of single steps is used for trajectory learning, then the robot can learn trajectories, but the process becomes very time-consuming and complicated
Solution Approach 1:
The system enables continuous trajectory recording by allowing the user to guide the robot arm through the entire trajectory in one continuous motion, rather than requiring step-by-step recording. The probe sensor continuously measures position and orientation data along the trajectory, enabling the robot to learn complex paths efficiently without interruptions or manual intervention at each step.
Solution Approach 2:
A probe sensor is introduced as an intermediary tool mounted on the robot arm to assist the user in controlling the tool center point. The probe sensor provides real-time feedback on distance and orientation relative to the trajectory, making continuous guidance easier and more intuitive, thereby reducing the time and complexity of trajectory learning.
2Manufacturing precision
If the robot moves in small steps to ensure precision, then the trajectory accuracy is maintained, but the learning process becomes complicated and time-consuming
Solution Approach 1:
The probe sensor provides continuous feedback on the position and orientation of the tool center point relative to the desired trajectory. This feedback mechanism allows the system to maintain high trajectory accuracy while enabling the user to guide the robot in continuous motion, eliminating the need for complex step-by-step control and reducing overall system complexity.
3Productivity
If continuous trajectory demonstration is enabled, then the learning process is simplified and faster, but precise control of position and orientation becomes more challenging
Solution Approach 1:
The probe sensor acts as an intermediary that simplifies continuous control by providing intuitive visual and tactile feedback to the user. It displays distance and orientation information in an easily interpretable format, making it simpler for the user to guide the robot arm through complex trajectories in continuous motion without requiring expert-level control skills.
Data Source
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AI summary
A robot learning system for trajectory learning of a robot (RB) having a robot arm between a base and a tool center point (TCP). A user interface allows the user to control the robot arm in order to follow a desired trajectory during a real-time. A probe sensor (PS) is mounted on the TCP during the learning session. The probe sensor (PS) measures a distance parameter (Z) indicative of distance from the TCP and a surface forming the trajectory to be followed, and an orientation parameter (X, Y) indicative of orientation of the TCP and the surface forming the trajectory to be followed. These distance and orientation data are provided as a feedback to the controller of the robot (CTL) during the real-time learning session, thereby allowing the robot controller software to assist the user in following a desired trajectory in a continuous manner. Especially, the probe sensor (PS) may have a displaceable tip (TP) to follow a surface and having a neutral or center position, and where the robot controller software controls the robot movements to seek the neutral or center position irrespective of the user's control inputs. Data (DT) is logged during the learning session, so as to allow later control of the robot (RB) in response to the data (DT) logged during the learning session.