Multi-axis Robotic Gripper Control via Sensory Maps
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Solution Overview
Problem
Conventional control methods for multi-axis robotic grippers are less than optimal for executing commanded grasp poses, particularly in complex tasks like bin picking, material handling, and part placement, due to their time-intensive and non-intuitive programming processes.
Innovation Solution
A system and method that utilize an interactive graphical user interface (GUI) and sensory maps to intuitively control multi-axis robotic grippers, allowing users to select and configure grasp poses through a touch-screen or similar interface, with sensory matrices providing calibrated limits for sensors, enabling intuitive programming and troubleshooting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional individual axis-and-sensor programming methods are used, then each sub-motion and sensor interaction can be explicitly defined, but the programming process becomes time-intensive and complex
Solution Approach 1:
The system uses sensory maps that store pre-recorded sensor data from demonstrated grasp poses. Instead of programming each axis and sensor interaction individually, the system copies the sensory patterns from a demonstrated pose and applies them to similar tasks, dramatically reducing programming time while maintaining precision through the structured sensory map framework
Solution Approach 2:
The system performs preliminary sensing and recording of grasp poses to create sensory maps before actual task execution. By pre-capturing sensor data from demonstrated poses and organizing it into structured maps with calibrated limits, the system prepares reusable knowledge that eliminates time-consuming individual programming of each motion and sensor interaction
2Adaptability or versatility
If multi-axis robotic grippers with sensory matrices are used, then complex grasp poses can be executed, but the control system becomes more complex and difficult to program
Solution Approach 1:
The control system is segmented into modular components: sensory maps that store grasp pose data, calibrated limits that define valid sensor ranges, and a controller that matches current sensor readings against these limits. This segmentation transforms the complex control of multi-axis grippers into manageable, reusable modules that simplify programming while maintaining full capability for complex grasp poses
Solution Approach 2:
The system changes the control approach from directly programming axis positions and sensor interactions to using sensory maps that define valid ranges of sensor readings. By transforming the control parameters from explicit motion commands to sensory pattern matching with calibrated limits, the system reduces control complexity while preserving the ability to execute complex grasp poses
3Productivity
If sensory maps with calibrated limits are implemented, then grasp pose execution becomes more intuitive and faster, but the initial setup and calibration requirements increase
Solution Approach 1:
The system uses self-service calibration where the robotic gripper demonstrates the desired grasp pose and the system automatically records the sensor readings to create the sensory map. The calibration process captures real sensor data from actual demonstrations, allowing the system to self-generate the calibrated limits and sensory patterns without requiring manual setup, thereby reducing initial complexity while enabling fast subsequent execution
Data Source
AI summary
A system includes a robotic gripper and a grasp controller. The gripper, which has a sensory matrix that includes a plurality of sensors, executes selected grasp poses with respect to a component in the corresponding method to thereby grasp the component in response to a grasp command signal from the controller. The controller has a touch-screen or other interactive graphical user interface (GUI) which generates a jog signal in response to an input from a user. Sensory maps provide calibrated limits for each sensor contained in the sensory matrix for the selected grasp pose. The controller transmits the grasp command signal to the gripper in response to receipt of the jog signal from the GUI. The GUI may display a jog wheel having icons, including a hub corresponding to a neutral pose of the robotic gripper and icons corresponding to grasp poses arranged around a circumference of the jog wheel.