๋์ ๋ ๋ก๋ด์ด ๊ถ๊ธํ๋ค๋ฉด?
Process Overview
This implementation case demonstrates the advancement of automated product picking and storage operations using 'Vulcan' robots introduced in an Amazon Logistics warehouse.
Amazon introduced tactile-based robots to simultaneously ensure efficiency and precision in repetitive and intricate storage tasks.
Vulcan utilizes AI and tactile sensor-based grippers that operate like human hands to automatically store products by adjusting pressure based on weight or material and efficiently utilizing space. This has further expanded the accuracy and scope of Amazon's Logistics automation.
Project Background and Purpose
While existing Logistics robot systems (G2P method) succeeded in automating tasks such as moving shelves, the task of organizing products on shelves still relied on manual labor.
Amazon developed and introduced Vulcan robots to reduce this repetitive work and implement advanced automation that considers space utilization and worker safety.
This project aims for the following:
Automation and advancement of product storage
Improvement in work precision through the application of cognitive-based robot technology
Ensuring worker safety and reducing musculoskeletal strain
๊ตฌ์ฑ์์
| ๋ก๋ด | UR Collaborative(Vulcan): Equipped with a gripper featuring tactile sensors, capable of detecting and storing delicate objects like a human. |
|---|---|
| ์ฃผ๋ณ๊ธฐ๊ธฐ | AI Storage System: Calculates optimal space layout to efficiently stack items like Tetris; Pressure/Torque Sensor: Adjusts pressure based on the material and shape of the items; Logistics Automation Integration Platform: Integrated operation with existing Kiva systems and Sparrow. |
์์ ์์
| STEP 1. | AI system searches for and calculates storage space |
|---|---|
| STEP 2. | Vulcan detects items via cameras and sensors |
| STEP 3. | utilizes pressure and torque sensors to pick up items with appropriate force |
| STEP 4. | places and stores items in the optimal location |
| STEP 5. | automatically switches to the next item to be stored. |
โป ๋ง๋ก์ ๋ด์ ๋ชจ๋ ์ฝํ ์ธ ๋ฅผ ๋ฌด๋จ์ผ๋ก ๋ณต์ฌ ๋ฐ ์ฌ์ฐฝ์ํ ๊ฒฝ์ฐ ๋ถ์ ๊ฒฝ์๋ฐฉ์ง๋ฒ ๋ฐ ์ ์๊ถ๋ฒ์ ์๋ฐ๋ ์ ์์์ ๋ฐํ๋๋ค.
์ถ์ฒ ๋์ ์ฌ๋ก
Heavy logistics automation success! Omron AMR automatically returns lead tabs ๐ฆ๐ค
Process Overview This case study demonstrates the automation of heavy-duty product transport using Omron AMR logistics robots . The client implemented a logistics automation system to reduce inefficiencies and labor burdens associated with repetitive, heavy logistics tasks. Specifically, they sought to reduce costs and maximize logistics efficiency through automated palletizing and return operations. This solution automates simple, repetitive tasks like product movement and loading, thereby increasing the efficiency and accuracy of the shipping process and reducing worker burden, thereby enhancing safety and productivity. Project Background and Objectives The client recognized the need for automation in heavy cart transport and lead tab return operations and sought to improve logistics operational efficiency and reduce operating costs by introducing AMRs. This project aimed to: Automating lead tab return and cart transport Improving worker productivity and ensuring safety Enhancing efficiency and flexibility in the logistics field
Yaskawa Industrial Robots + Automatic Foam Placement โ Hankook & Company Battery Palletizing Automation Case ๐๐ค
Process Overview Hankook & Company's palletizing solution prioritizes higher output and reduced labor requirements. After removing the existing line that was built around a single palletizer, the operation was split into two lines and reconfigured with two palletizers to secure production capacity (CAPA). In addition, manual placement of styrofoam and slip sheets was automated to reduce operator interventionโenabling one operator to manage both lines. Project Background and Objectives Background The previous workflow had an imbalance between line production capacity (CAPA) and palletizing capacity (CAPA), requiring operators to manually support styrofoam and slip-sheet placement to maintain output. This structure forced continuous loading of slip sheets (approx. 7 kg each), causing significant operator fatigue. Also, the line had to wait until manual slip-sheet loading was completed, leading to large fluctuations in the logistics cycle time. Key issues Palletizing quality instability and speed loss caused by manual operations Reduced productivity, increased operator fatigue, and skilled-labor dependency issues Objectives Improve production efficiency by minimizing repetitive manual tasks through upgraded robot automation Ensure quality consistency by reducing manual intervention Improve the work environment by reducing operator fatigue and strengthening safety
An order picking system solution for logistics warehouses utilizing AMRs
Process Overview This case study demonstrates an order picking system that optimizes the operation of ten Eugene Gocart 180s using Marosol's remote control system, SOLlink. With the self-developed SOLlink, multiple AMRs are clustered and controlled to maximize logistics system efficiency. The order picking system, integrated with WMS, allows for flexible handling during peak periods without the need for additional workers. Warranty Period 1 Year Performance Year 2023 Project Duration Need for consultation Project Background and Objectives Improved workforce supply and employment stability through the introduction of logistics robots Introducing robots for high-intensity, repetitive tasks reduces the risk of musculoskeletal disorders and industrial accidents among workers Integrating WMS and order picking systems increases work efficiency and reduces error rates
Process Overview
This implementation case demonstrates the advancement of automated product picking and storage operations using 'Vulcan' robots introduced in an Amazon Logistics warehouse.
Amazon introduced tactile-based robots to simultaneously ensure efficiency and precision in repetitive and intricate storage tasks.
Vulcan utilizes AI and tactile sensor-based grippers that operate like human hands to automatically store products by adjusting pressure based on weight or material and efficiently utilizing space. This has further expanded the accuracy and scope of Amazon's Logistics automation.
Project Background and Purpose
While existing Logistics robot systems (G2P method) succeeded in automating tasks such as moving shelves, the task of organizing products on shelves still relied on manual labor.
Amazon developed and introduced Vulcan robots to reduce this repetitive work and implement advanced automation that considers space utilization and worker safety.
This project aims for the following:
Automation and advancement of product storage
Improvement in work precision through the application of cognitive-based robot technology
Ensuring worker safety and reducing musculoskeletal strain
๊ตฌ์ฑ์์
| ๋ก๋ด | UR Collaborative(Vulcan): Equipped with a gripper featuring tactile sensors, capable of detecting and storing delicate objects like a human. |
|---|---|
| ์ฃผ๋ณ๊ธฐ๊ธฐ | AI Storage System: Calculates optimal space layout to efficiently stack items like Tetris; Pressure/Torque Sensor: Adjusts pressure based on the material and shape of the items; Logistics Automation Integration Platform: Integrated operation with existing Kiva systems and Sparrow. |
์์ ์์
| STEP 1. | AI system searches for and calculates storage space |
|---|---|
| STEP 2. | Vulcan detects items via cameras and sensors |
| STEP 3. | utilizes pressure and torque sensors to pick up items with appropriate force |
| STEP 4. | places and stores items in the optimal location |
| STEP 5. | automatically switches to the next item to be stored. |
โป ๋ง๋ก์ ๋ด์ ๋ชจ๋ ์ฝํ ์ธ ๋ฅผ ๋ฌด๋จ์ผ๋ก ๋ณต์ฌ ๋ฐ ์ฌ์ฐฝ์ํ ๊ฒฝ์ฐ ๋ถ์ ๊ฒฝ์๋ฐฉ์ง๋ฒ ๋ฐ ์ ์๊ถ๋ฒ์ ์๋ฐ๋ ์ ์์์ ๋ฐํ๋๋ค.
๋์ ๋ ๋ก๋ด์ด ๊ถ๊ธํ๋ค๋ฉด?
์ถ์ฒ ๋์ ์ฌ๋ก
Heavy logistics automation success! Omron AMR automatically returns lead tabs ๐ฆ๐ค
Yaskawa Industrial Robots + Automatic Foam Placement โ Hankook & Company Battery Palletizing Automation Case ๐๐ค