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BMW Group Selects NVIDIA to Redefine Factory Logistics

Auto News - Published on Mon, 18 May 2020

Image Source: BMW Logistics Robots NVIDIA
BMW Group’s supply chain takes millions of parts flowing into a factory from more than 4,500 supplier sites, involving 230,000 unique part numbers. Moreover, BMW Group vehicles are offered to customers with an average of 100 different options, resulting in 99 percent of customer orders being uniquely different for each other. This creates an immense challenge for factory logistics. BMW Group has selected the new NVIDIA Isaac robotics platform to enhance its automotive factories, utilizing logistics robots built on advanced AI computing and visualization technologies, the companies announced today. BMW Group’s objective is to enhance logistics factory flow to produce custom-configured cars more rapidly and efficiently. Once developed, the system will be deployed to BMW Group factories worldwide.

To optimize the enormous complexity of this material flow, autonomous AI-powered logistics robots now assist the current production process in order to assemble highly customized vehicles on the same production line.

The collaboration centers on implementing an end-to-end system based on NVIDIA technologies, from training and testing through to deployment, with robots developed using one software architecture, running on NVIDIA’s open Isaac robotics platform.

The collaboration uses NVIDIA DGX AI systems and Isaac simulation technology to train and test the robots; NVIDIA Quadro ray-tracing GPUs to render synthetic machine parts to enhance the training; and a new lineup of multiple AI-enabled robots built on the Isaac software development kit, powered by high-performance NVIDIA Jetson and EGX edge computers.

Developed on the NVIDIA Isaac SDK, the robots utilize a number of powerful deep neural networks, addressing perception, segmentation, pose estimation and human pose estimation to perceive their environment, detect objects, navigate autonomously and move objects. These robots are trained both on real and synthetic data using NVIDIA GPUs to render ray-traced machine parts in a variety of lighting and occlusion conditions to augment real data.

The real and synthetic data are then used to train deep neural networks on NVIDIA DGX systems. The robots are then continuously tested in NVIDIA’s Isaac Simulators for both navigation and manipulation, operating on NVIDIA’s Omniverse platform, where multiple BMW Group personnel in different geographies can all work in one simulated environment.

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Posted By : Yogender Pancholi on Mon, 18 May 2020
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