3D Vision System for Teat Detection in Robotic Milking
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Solution Overview
Problem
Existing dairy milking systems face challenges in accurately positioning and maneuvering robotic arms due to the unpredictable movement of dairy livestock, such as cows, and the variability in teat positions, which leads to inefficiencies and inaccuracies in teat cup attachment.
Innovation Solution
A vision system incorporating a robotic arm, laser, and processor that uses 3D images and profile signals to detect and compensate for leg and teat movement, avoid obstacles like the tail, and accurately identify teat positions, allowing for real-time adjustments and improved teat cup attachment methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hard-coded movements and positions are used for robotic arm operations, then the system structure is simple, but the accuracy of teat cup attachment deteriorates due to unpredictable animal movement
Solution Approach 1:
The system performs preliminary scanning and detection of teat positions using a laser and vision system before the robotic arm executes the attachment operation. Historical teat location information is stored and used to predict expected teat positions, allowing the system to prepare and adjust before actual attachment occurs.
Solution Approach 2:
The vision system continuously scans the dairy livestock and provides real-time feedback on teat positions and animal movement. The processor compares detected positions with expected positions from historical data, and the robotic arm adjusts its movements based on this feedback to maintain accurate teat cup attachment.
2Reliability
If real-time detection and compensation for animal movement is implemented, then the reliability of teat cup attachment is improved, but the device complexity increases
Solution Approach 1:
The system performs preliminary scanning and detection of teat positions using a laser and vision system before the robotic arm executes the attachment operation. Historical teat location information is stored and used to predict expected teat positions, allowing the system to prepare and adjust before actual attachment occurs.
Solution Approach 2:
The vision system continuously scans the dairy livestock and provides real-time feedback on teat positions and animal movement. The processor compares detected positions with expected positions from historical data, and the robotic arm adjusts its movements based on this feedback to maintain accurate teat cup attachment.
3Measurement precision
If multiple scans are performed to verify teat position, then the measurement precision is improved, but the time required for attachment increases
Solution Approach 1:
The system performs a limited number of scans (first and second scans, then third and fourth scans) to verify teat positions rather than continuous scanning. This partial action approach provides sufficient verification to achieve the desired measurement precision while constraining the time expenditure to a practical level.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The vision system enhances the accuracy and efficiency of robotic arm operations by enabling real-time detection and compensation for animal movement, improving teat cup attachment success rates and reducing the need for hard-coded movements and positions.
Implementation Method 1
The laser is coupled to the robotic arm and is configured to generate a plurality of profile signals, each profile signal comprising information associated with a relative distance between the laser and at least a portion of the dairy livestock
Data Source
AI summary
A vision system that includes a robotic arm and a three-dimensional (3D) camera operably coupled to a processor. The processor is configured to position the robotic arm adjacent to the dairy livestock and acquire a 3D image using the 3D camera. The processor is further configured to identify a set of teat candidates within the 3D image and to filter the set of teat candidates based on one or more filtering rules. The processor is further configured to determine an aggregate teat candidate score for the consolidated set of teat candidates, compare the aggregate teat candidate score to a score threshold value, and update teat location information for the dairy livestock in response to determining the aggregate teat candidate score is greater than or equal to the score threshold value.