How Data Labeling Service Empowers Logistics Robot? — Part2

ByteBridge
Sep 15 · 6 min read
dazzlemagnet.com

The Intelligent Transformation of Logistics is Accelerating

Nowadays, the intelligent transformation of logistics is an inevitable trend, which also means that the development prospects of logistics robots would be bright.

As various technologies of logistics robots, including intelligent navigation and control technology, continue to mature, the diversification of their applications and scenarios will become a development trend.

Therefore, logistics robot companies need to segment the market and combine new technologies such as AI to create highly intelligent and flexible products and solutions.

The application of unmanned logistics robots will no longer be limited to warehousing and will be used for transshipment and terminal distribution to better solve the problem of complicated manual labor and improve efficiency.

Undoubtedly, with the help of capital, policies, and markets, the logistics robot industry will become a new battlefield for players from all walks of life.

In Fact, this is only a Microcosm of the Intelligent Transformation of Logistics.

With the popularization of intelligent equipment such as unmanned logistics vehicles, AGV, AMR sorting robots, and intelligent distribution systems, and the empowerment of intelligent IoT terminal operating systems, the blockages of the entire logistics transportation industry chain are constantly being opened up, and the operating efficiency will continue to be improved.

This allows express logistics companies to calmly deal with the huge order volume. Enterprises use digital and intelligent technologies to revitalize every pipeline in the logistics ecological chain and bring consumers an increasingly better experience. In the next 1–2 years, the entire logistics industry will usher in the first intelligent year.

Technological Innovation is the Core Force of Enterprise Development

At present, the players on the logistics robot are still mainly e-commerce companies and AI emerging firms.

In comparison, the former not only has introduced palletizing robots, sorting robots, and other products in the field of warehousing and logistics, but also has deployed in the field of terminal distribution, using closed-loop intelligent services from warehousing to distribution, and better e-commerce business development.

The latter focuses on the transportation pipeline of warehousing and promotes the development of intelligent logistics. However, under the spoil of capital, the logistics robot market is surging undercurrent, and relevant companies should continue to seek technological innovation.

As far as the warehousing field is concerned, in the early days, due to the many pain points in the traditional warehousing, handling, and sorting operations, many companies began to use AGV robots. However, with the rapid development of the e-commerce market, a single small number of fixed-path robots have become difficult to meet the requirements of logistics automation and cost reduction.

Robots have already developed from traditional AGVs to multi-functional handling robots, and even climbing handling robots.

Compared with traditional AGVs, AMR has functions such as environment perception, autonomous navigation, intelligent obstacle avoidance, and intelligent following, which can eliminate the need of the workforce to rebuild the environment such as laying guide wires and attaching landmarks, especially when the production process is alternated.

At the same time, it also breaks the boundaries of AGV capabilities and can complete handling, selection, and sorting tasks in different scenarios. Especially in large-scale cluster scheduling and complex work scenarios, AMR equipped with VSLAM has more advantages.

In this regard, the industry also generally believes that AMR based on SLAM technology is an important technology development direction for logistics robot companies. It is foreseeable that as the warehouse allocation model moves forward, AMR will be able to better leverage its advantages, and market capacity will gradually open up.

However, AMR is still in the early stage of implementation. In the future, whether companies can clear users' pain points, whether the core technologies can be quickly transplanted, whether product platformization can be realized, is the key to winning the next competition.

Without Labeled Data, there is no AI

From the perspective of the research direction of artificial intelligence technology, whether in the field of traditional machine learning or deep learning, supervised learning based on training data is still a major model training method. Especially in the field of deep learning, more labeled data is needed to improve the effectiveness of the model.

With the acceleration of the commercialization of AI and the application of AI technologies such as assisted driving and customer service chatbot in all walks of life, the expectation of data quality in the special scenarios is getting higher and higher. High-quality labeled data would be one of the core competitiveness of AI companies.

If the general datasets used by the previous algorithm model are coarse grains, what the algorithm model needs at present is a customized nutritious meal. If companies want to further improve certain model’s commercialization, they must gradually move forward from the general dataset to create their own unique one.

More info: Eight Common Data Annotation and Labeling Tools

  • Object Recognition
  • Object Tracking in Video
  • Industrial Robot Navigation
  • Robot Arm Guidance Crack Detection

Robotic sorting and material handling, like Package Delivery, Warehouse Inventory Handling

QA Check, Maintenance

ByteBridge, a human-powered and ML-powered data labeling tooling platform

ByteBridge is a data labeling SAAS platform with robust tools and real-time workflow management. It provides high-quality training data for the machine learning industry.

  • 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.
  • Consensus — Assign the same task to several workers, and the correct answer is the one that comes back from the majority output.
  • All results are thoroughly assessed and verified by a human workforce and machine
ByteBridge, a Human-powered and ML-powered Data Labeling Tooling Platform

In this way, ByteBridge can affirm the data acceptance and accuracy rate is over 98%.

On ByteBridge’s SaaS dashboard, developers can start the labeling projects by using the labeling instruction template and get the results back instantly.
From online setting labeling briefing to expert support alongside, the instruction communication is not that hard anymore.

ByteBridge Labeling Instruction Template

Here is the Labeling Instruction Template Guideline:

ByteBridge Data Labeling Platform Beginner Operational Guideline

As a fully managed platform, it enables developers to manage and monitor the overall data labeling process and provides API for data transfer. The platform also allows users to get involved in the QC process.

ByteBridge, a Human-powered and ML-powered Data Labeling Tooling Platform

These labeling tools are available: 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.

End

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: support@bytebridge.io

Source: https://www.163.com/dy/article/GILMKPNU0552HYDR.html

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