How to optimize intra logistics to streamline and speed industry 4.0
Part 1 of this series on intra logistics discussed issues related to how autonomous mobile robots (AMRs) and automated guided vehicles (AGVs) are used on a system level for implementing intra logistics and quickly and safely moving materials as needed. This article focuses on use cases and how AMRs and AGVs employ sensors to identify and track items, how machine learning (ML) and artificial intelligence (AI) support material identification, movement and delivery of materials throughout warehouse and production facilities. Intra logistics (internal logistics) uses autonomous mobile robots (AMRs) and automated guided vehicles (AGVs) to efficiently move materials around Industry 4.0 warehouses and production facilities. To streamline and speed supply chains, intra logistics systems need to know the current location of material, the intended destination of material, and the safest, most efficient path for the material to reach the destination. This streamlined navigation requires a diversity of sensors. In intra logistics solutions, AGVs and AMRs use sensors to increase their situational awareness. Arrays of sensors provide safety for nearby personnel, protection of other equipment, and efficient navigation and localization. Depending on application
requirements, sensor technologies for AMRs can include contact sensors like limit switches built into bumpers, 2D and 3D light detection and ranging (LiDAR), ultrasonics, 2D and stereo cameras, radar, encoders, inertial measurement units (IMUs), and photocells. For AGVs, sensors can include magnetic, inductive, or optical line sensors, as well as limit switches built into bumpers, 2D LiDAR, and encoders. The first article of this series covers issues related to how AMRs and AGVs are used at a system level for implementing intra logistics and efficiently moving materials as needed. This article is focused on sensor fusion and how AMRs and AGVs employ combinations of sensors plus AI and ML for localization, navigation, and operational safety. It begins with a brief review of common sensors found in AGVs, examines robot pose and simultaneous location and mapping (SLAM) algorithms using sensor fusion, considers how SLAM estimates can be improved with scan-to-map matching and scan- to-scan matching techniques, and closes with a look at how sensor fusion contributes to safe operation for AMRs and AGVs. DigiKey supports designers with a wide range of sensors and switches for robotics and other industrial applications in all these cases.
supply chains – part two of two
By Jeff Shepard Contributed By DigiKey's North American Editors
we get technical
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