Issue link: https://resources.mouser.com/i/1442859
Texas Instruments The Future of Robotics | 21 Figure 1: The different senses of a robot. The Growth of AI Robotic automation has been a revolutionary technology in the manufacturing sector for some time, yet the integration of AI into robotics is poised to transform the industry over the next few years. According to research from Markets And Markets, the robotic automation technology market will grow from about $270 million (USD) in 2016 to just under $4.9 billion by 2023. Consulting firm Accenture's Institute for High Performance researched the impact of AI in 12 developed economies. The study found that AI could double annual economic growth rates by 2035, improve labor productivity by as much as 40 percent, and change the nature of work by spawning a new hybrid relationship between human and machine, driving creativity, innovation and growth in collaboration with AI-driven robots. This paper explores some of today's key trends in robotics and automation and the most important technologies that will tie AI to the data that it needs to be intelligent. It will also address how Texas Instruments (TI) sensors are being used (and fused) into AI systems, and how TI offers a broad portfolio of sensor and data- processing components that covers the entire AI system signal chain for robotics. Pushing AI Processing for Robotics to the Edge There are two main parts of ML: training and inference, which can be executed on completely different processing platforms. The training side usually occurs off-line on desktops or in the cloud, and entails feeding large data sets into a neural network. Real-time performance or power is not an issue during this phase. The result of the training phase is a trained AI system that when deployed can perform a specific task, such as inspecting a bottle on an assembly line, counting and tracking people within a room, or determining whether a bill is counterfeit. But for AI to fulfill its promise in many industries, the fusion of sensor data that happens during inference (the part that executes the trained ML algorithm) must happen in (near) real time. Thus, designers need to put ML and deep-learning models on the edge, deploying the inference into an embedded system. For example, a cobot is built to work in close collaboration with humans. It relies on data from proximity sensors as well as vision sensors to ensure that it successfully protects humans from harm while supporting them in activities that would be challenging for them. All of this data needs to be processed in real time, but the cloud is not fast enough for the real-time, low-latency response that the cobot needs. To address this bottleneck, today's advanced AI systems are pushed to the edge, which in the case of robots means onboard. The decentralized AI model This decentralized AI model relies on highly integrated processors that have: • A rich peripheral set for interfacing to various sensors. • High-performance processing capability to run machine- vision algorithms. • A way to accelerate deep-learning inference. All of these capabilities also have to work efficiently and with a relatively low-power and small-size footprint in order to exist at the edge. Power- and size-optimized inference engines are increasingly available as ML grows in popularity. These engines are specialized hardware offerings aimed specifically at performing ML inference. An integrated system-on-chip (SoC) is often a good choice in the embedded space, because in addition to housing various processing elements capable of running deep-learning inference, an SoC also integrates many components necessary to cover the entire embedded application. Some integrated SoCs include display, graphics, video acceleration and industrial networking capabilities, enabling a single-chip solution that does more than just run ML/AI.