Machine
Learning
Background
Caja Robotics’ Machine Learning
When Caja aims to automate a warehouse, we know your warehouse is unique in many ways. But that doesn’t mean our system needs to learn everything from scratch. Information gathered from general imagery databases allows quick learning. We don’t need to re-learn what a damaged bin or cracked floor looks like, for example, so learning from existing databases saves time. Our system does need to learn your specific warehouse operation, however. To save significant time before even changing one bolt in the warehouse, we create simulations, but not the kind you imagine. Caja Robotics’ simulations are accurate to the second. As our backend system controls every aspect of the warehouse operation, including each robot’s navigation, our Machine Learning begins optimizing the operation before the warehouse even exists.
Machine Learning advantages
Image
recognition
Caja’s robot ‘playground’ is an enclosed and human-free environment, allowing for high speed during both inbound and outbound activity. When the option for unplanned obstacles, such as people, is minimized, our robots can reach much higher speeds. Just think of the difference in speed you can achieve when driving on a highway vs. driving in a pedestrian zone.
Dynamic
navigation
Machine Learning with large data processing enables smarter, more dynamic navigation. Since our software controls and determines warehouse operation and navigational routes, our system can see and act for the warehouse’s larger needs. This means no simple A to B navigation to achieve the shortest routes, rather dynamic, adaptable routes that will achieve the fastest and best overall warehouse functionality.
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