Overview

Freight volumes in the logistics industry have dramatically increased in recent years due to the worldwide spread of e-commerce. As a result, the lack of processing capacity due to constant labor shortages has become a chronic and serious problem.
Because of those, distribution centers are rapidly moving to increase automation and reduce manual labor. Machine vision is attracting attention as a viable solution. In addition to the identification and sorting of goods using barcode readers, a wide variety of image sensors are being used in various logistics processes, from the picking of goods using robots, to the automatic conveyance of goods in warehouses, and delivery. We are also witnessing the adoption of AR glasses to ensure that the picking of all goods by workers is carried out accurately, speedily, and free of human error from their designated locations.

Applications

Code recognition and sorting

Global shutter image sensors are widely used at processes used to sort goods on conveyors moving at high speeds due to their ability to capture distortion-free code images. On the other hand, with applications that do not require high-speed scanning, rolling shutter image sensors are also being used for their ability to capture images with low noise, even under low light intensity conditions.

Code recognition and sorting

Goods picking in warehouses

By using AR glasses, workers are able to immediately confirm the location and quantity of the goods they are looking for, allowing them to swiftly and reliably pick goods. AR glasses are also expected to improve productivity by allowing workers to carry out work hands free, and to reduce work errors. High-contrast, high-visibility OLED Microdisplays are used in AR glasses such as these.

Goods picking in warehouses

Robot picking

Calculating three-dimensional information is important for picking parts using robot arms.
Stereo camera systems using two image sensors to obtain three-dimensional information are widely used, but by using ToF image sensors capable of obtaining three-dimensional information with a single camera, a more compact system configuration can be realized.

Event-based vision sensors that output light data in high speed, detecting changes in the luminance of each pixel and combining “coordinate” and “time” information, can be used for three-dimensional measurement and machine learning that does not require color information.

Robot picking

AMR (Autonomous Mobile Robots)

In large distribution warehouses and plants, the need for automatic conveying equipment to transport goods to their destination in place of workers has been increasing. Conveyors with guides have generally been used up to now, but in recent years, autonomous travel AMRs that do not require guides have been gaining much attention.

ToF image sensors can be used to detect automatic transfer robot collisions by harnessing the characteristics of three-dimensional sensors. Moreover, event-based vision sensors are also capable of detecting external features of objects from changes in luminance, allowing obstacles to be detected at high speed. They are expected to be used for automatic transportation in warehouses.

AMR (Autonomous Mobile Robots)

Delivery robots

There is increasing attention and expectations for home delivery robots to solve issues such as the cost and speed of the "last mile" to the delivery destination.

Event-based vision sensors and ToF image sensors are gaining much attention as sensors useful for such robot collision detection or external situation recognition.

Delivery robots

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