Robot Gripper Sensor Positioning for Accurate Object Classification
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Solution Overview
Problem
Robotic systems face challenges in accurately collecting sensor data about objects in their environment, particularly when tasks require close proximity to the object, leading to increased system complexity and potential failure points due to the use of remote sensors.
Innovation Solution
Equipping robotic grippers with non-contact sensors that can be positioned at predetermined poses relative to objects, using machine learned classifiers to analyze sensor data from these close-up positions, allowing for more accurate object classification and manipulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If remote sensors are used to collect sensor data about objects, then the robotic system can operate from a distance, but the measurement precision and accuracy of object properties deteriorate
Solution Approach 1:
The robotic gripper is made dynamically positionable relative to the object, allowing the sensor to move from a remote position to a close-proximity position when needed. This dynamic repositioning enables the system to achieve high measurement precision without permanently increasing device complexity, as the sensor only approaches the object when sensor data is required.
Solution Approach 2:
The robotic gripper serves dual functions: both manipulating the object and positioning the sensor for data collection. By integrating the sensor into the gripper assembly, the system eliminates the need for separate sensor positioning mechanisms, thereby reducing overall device complexity while maintaining measurement precision.
2Measurement precision
If sensors are positioned at close proximity to objects, then sensor data accuracy improves, but the number of potential failure points increases
Solution Approach 1:
The robotic gripper is designed with multi-functionality, serving both as an object manipulation tool and as a sensor positioning mechanism. This universal design reduces the total number of components in the system, thereby decreasing potential failure points while enabling close-proximity sensing for improved object classification accuracy.
3Measurement precision
If multiple sensors are used to collect comprehensive sensor data, then object classification accuracy improves, but the device complexity increases
Solution Approach 1:
Instead of using multiple static sensors, the system employs a single sensor that dynamically repositions itself to multiple locations relative to the object through the robotic gripper. This dynamic approach achieves comprehensive object classification accuracy equivalent to multiple sensors while maintaining lower device complexity.
Data Source
AI summary
In one embodiment, a method includes receiving, from a first sensor on a robot, first sensor data indicative of an environment of the robot. The method also includes identifying, based on the first sensor data, an object of an object type in the environment of the robot, where the object type is associated with a classifier that takes sensor data from a predetermined pose relative to the object as input. The method further includes causing the robot to position a second sensor on the robot at the predetermined pose relative to the object. The method additionally includes receiving, from the second sensor, second sensor data indicative of the object while the second sensor is positioned at the predetermined pose relative to the object. The method further includes determining, by inputting the second sensor data into the classifier, a property of the object.