In the previous article, we talk about what is a robot and the intelligent requirement of the robot, in this article, we extend the new role of industrial robots.
With the continuous expansion of industrial robots in the industrial field, new requirements for robots are constantly put forward, which promotes AI technology. Meanwhile, with the increasing intelligent level, the robot has been replacing human roles largely in production, and widely engaged in various operations, becoming the main labor force in production activities. The new role of industrial robots, from replacing most parts of human physical labor to part of mental labor, and then continue to an integrated automation production systems, make an impact on management coordination.
Isolated robots have no practical value in production. The role of robots in production is actually performed by different end effector (gripper) structures and functions according to the product content, product structure, process requirements, and other factors. The robot realizes various independent or composite operation functions such as loading and unloading, assembling, processing, detection, and intelligent inspection.
Therefore, the high-efficiency robot end effector (gripper), the auxiliary mechanical device to continuously extend the robot’s action space, and the intelligent ability to enhance the robot’s vision, perception, detection, and analysis are the important attributes in industrial robots performance evaluation.
For the robots that perform production processing and generate the added value of products, their claws integrate all kinds of processing tools (welding gun, spray gun, cutting tool, grinding wheel and polishing wheel, etc.) to realize the production of welding, coating, grinding, detection and assembly. They play an irreplaceable role in the production process, such as process, precision, production efficiency, etc. However, for those auxiliary robots which cooperate with the machine tool or other automation equipment, or independently realize the material taking and discharging, the structure and function of the claw are relatively single, and there is no obvious advantage in efficiency, quality, and other aspects. As they undertake the auxiliary work and they can be replaced.
At present, about 75% of the industrial robots widely used in all aspects of production are engaged in the auxiliary work of simply taking and placing materials. Therefore, it is still mainstream for robots to undertake supporting roles.
If all kinds of claws with different structures and functions make industrial robots play an important role in production, adding auxiliary mechanical devices and installing robots on movable sliding tables, rails or rollers will expand the space of robots, which is no different from installing legs and wings on robots and realizing one or more dimensional movements.
With hands and feet, and then equipped with CCD, sensors, and other visual sensing devices, robots seem to have eyes and brains. In other words, they have the functions of visualization, analysis, and judgment, system feedback, etc. Moreover, they can realize intelligent work such as analysis and judgment, fault handling, production management in the integrated environment.
The algorithm, computing power, and data are the three basic elements of the development of an intelligent robot. Just as a triangle needs three sides to stabilize its shape, artificial intelligence will also need all three elements to perfect itself.
Among them, data is the foundation, which provides the underlying support for the algorithm. If you compare an algorithm to a car, data is the fuel that drives the car forward.
At present, AI enterprises have to go through three stages: research and development, training, and implementation, and each stage requires the support of massive basic data sets.
In machine learning, with each round of testing, engineers would discover new possibilities to perfect the model performance, therefore, the workflow changes constantly. There are uncertainty and variability in data labeling. The clients need workers who can respond quickly and make changes in workflow, based on the model testing and validation phase.
Therefore, High-quality labeled data for machine learning algorithms training has become the core part of artificial intelligence development in recent years.
Common Labeling Tools in Robotics
Common Labeling Types
- Object Recognition
- Object Tracking in Video
- Semantic Segmentation
With real-time workflow management, Bytebridge can provide high-quality data with accuracy and efficiency:
- ML-assisted capacity can help reduce human errors by automatically pre-labeling
- The real-time QA and QC are integrated into the labeling workflow as the consensus mechanism is introduced to ensure accuracy.
- All work results are completely screened and inspected by the machine and human workforce.
In this way, ByteBridge can affirm the data acceptance and accuracy rate is over 98%.
- Clients can set labeling rules, iterate data features, attributes, and task flows, scale up or down, make changes.
For example, you can choose a Polygon and Classification Template on the dashboard:
Clients can monitor the labeling progress and get the results in real-time on the dashboard.
These labeling tools are already available on the dashboard: Image Classification, 2D Boxing, Polygon, Cuboid.
We can provide personalized annotation tools and services according to customer requirements.
A collaboration of the human-work force and AI algorithms ensure a 50% lower price compared to the conventional market.
If you need data labeling and collection services, please have a look at bytebridge.io, the clear pricing is available.
Please feel free to contact us: firstname.lastname@example.org
1 How the Data Labeling Service Empowers Robotics in 2021? — Part1
2 Importance of High-Quality Training Data in Different AI Algorithm Stage
3 How Data Annotation Service Accelerates AI Application in the Field of Industry?
4 Data Labeling Service — Four Customer Pain Points in Getting No Bias Training Data?
5 Why the High-Quality Training Data is so Important to AI Machine Learning?