The RobotCore framework helps in leveraging hardware acceleration and building custom compute architectures for robots, or IP cores, which make the robots faster, more deterministic, and/or power efficient. RobotCore is a robot-specific processing unit that helps map Robot Operating System (ROS) computational graphs to its CPUs, GPU, and FPGA efficiently to get the best performance. The RobotCore framework is intended to be a modular and extendable framework.
Before moving forward, let us be clear about hardware acceleration. Hardware acceleration is the process of offloading certain computational task to specialised hardware so that you can perform those tasks more efficiently. For example, let us say you are using a simulation software or playing a game, then you would want graphical processing units (GPUs) in your system that could handle the render with ease.
That is what GPU-enabled hardware acceleration is when it comes to simulation software and games. In the context of robotics, hardware acceleration can help you create faster and more power-efficient robots. This is done through various accelerator platforms.
Let us now understand how hardware acceleration is used in robotics. We all have seen the Atlas robot by Boston dynamics. The Atlas uses hardware acceleration in the perception stack to navigate through the obstacles. It uses a time of flight sensor at high frequency to detect and extract surfaces from the environment and then use the navigation stack to navigate and control stack to move through the environment.
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