Robots and Physical Computing

Popular Posts starting with NVIDA jetson nano This is the third of a planned occasional series of posts on playing with some of the current AI specific boards for Intenet of Things ... Coral Dev Board and Raspberry Pi This is the second of a planned occasional series of posts on playing with some of the current AI specific add-on processors for Intenet of ... Moving Eyes with an Adafruit Adabox kit One of the things I enjoy is a subscription to Adabox from Adafruit, receiving the box and very soon before that finding out what the main ... Top 10 posts on the Robots and Physical Computing Blog in August 2019 Popular Posts Coral Accelerator on a Raspberry Pi This is the first of a planned occasional series of posts on playing with some ... How to produce a Microbit neural network This is really part two of a set of post  in response to a question from Carl Simmons ( @Activ8Thinking ) concerning building a   micro:bit ... ...

One of the things I enjoy is a subscription to Adabox from Adafruit, receiving the box and very soon before that finding out what the main item is the box is a treat. The latest, at the time of writing, is AdaBox 13 with the MONSTER M4SK a set of small screens, based around  240x240 IPS TFT displays,  acting as moving eyes in a mask, along with a couple of masks, speaker, microphone, lenses, etc and craft materials for decorating a mask - a full list can be found here . My goal was, to play with this, was to create a slightly creepy mask where the eyes move - a simple build but fun to do. The Adafruit quick start guide for this (available here ) provides all the instructions on setting it and downloading the different sets of eyes (that is really creepy to write). A set of different graphics files are already available for different sets of eyes;  a couple of examples are shown below.  One suggestion is when you download the files for the eyes ...

This is the third of a planned occasional series of posts on playing with some of the current AI specific boards for Intenet of Things (IoT). In the series, it is planned that will be some experiments with the Google Coral accelerator adapter and the Development Board; as well the NVIDIA Jetson Nano.  In previous posts I started playing the Coral Accelerator adapter ki t and the Coral Development Board. This post looks a starting with the NVIDIA Jetson Nano Development Kit   which like the Coral Development Board is a small computer designed for running combined embedded and neural network applications. The processing power comes from  a quad-core 64-bit ARM CPU and a 128-core integrated NVIDIA GPU (for more details see here ) So before we all get spooked; getting going is relatively easy, basically, follow  https://developer.nvidia.com/embedded/learn/get-started-jetson-nano-devkit#intro . Following these instructions I would suggest if you are able to...

This is the second of a planned occasional series of posts on playing with some of the current AI specific add-on processors for Intenet of Things (IoT). In the series, it is planned that some experiments with the Google Coral adapter and the Development Board; as well the NVIDIA Jetson Nano will be shown. In the previous post I started playing with the  Coral Accelerator with a Raspberry Pi  https://robotsandphysicalcomputing.blogspot.com/2019/08/coral-accelerator-on-raspberry-pi.html .  The Coral environment is related to Google's earlier AIY Edge Tensor Processing Unit (TPU) range  https://aiyprojects.withgoogle.com/edge-tpu/  and designed to work with  TensorFlow Lite . In this post, the bigger sibling the Coral Development Board or Coral Dev Board is connected to a Raspberry Pi. The Coral Dev board is a single board Linux computer in its own right, running a derivative of Debian called Mendel. The Pi (in this case I used A Raspberry Pi 2 running Rasp...

Popular Posts Coral Accelerator on a Raspberry Pi This is the first of a planned occasional series of posts on playing with some of the current AI specific add-on processors for Intenet of T... Produce a Microbit python neural network 2: Building a Physical Microbit Neural Network This is second in a two-post series on building a neural network using microbits with micropython. In the first post python was used to pro... Produce a Microbit python neural network 1: Without the microbit initially These two posts are really extensions of a set of post in response to a question from Carl Simmons ( @Activ8Thinking ) concerning building a... Micro:bit, Servo control with Micropython or blocks You can control servos (small ones) from a Micro:Bit directly. Following a link from the David Whale (Twitter  @ whaleygeek ) , thank you, t... USB Robot arm control with Python and Raspberry Pi I was asked recently if the USB robot ar...

This is the first of a planned occasional series of posts on playing with some of the current AI specific add-on processors for Intenet of Things (IoT). In the series, it is planned that some experiments with the Google Coral adapter and the Development Board; as well the NVIDIA Jetson Nano will be shown. Why bother? Basic reason is I love playing with AI and hardware - so it is kind of fun. Another reason is AI, IoT and e dge computing, are important and growing technologies, and I want to start getting my head around them a bit. In this post, I look at starting to use Coral Accelerator with a Raspberry Pi. The Coral environment is related to Google's earlier AIY Edge Tensor Processing Unit (TPU) range  https://aiyprojects.withgoogle.com/edge-tpu/  and designed to work with TensorFlow Lite . Good place to start is Google's Get started with the USB Accelerator  pretty much all you need to do to get going is in it, it also mentions Raspberry Pi. It makes a good ...

This is second in a two-post series on building a neural network using microbits with micropython. In the first post python was used to produce a neural network without the microbits. In this post the network is as shown in figure 1 is developed. The figure below shows the arrangement of the connections to be built; pin 2 is the output of each neuron. The two micro:bits/neurons on the left of the picture taking in the two same inputs ; the output from these neurons are the two inputs to the output neuron on the right. figure 1 The micro:bit objects used in Figure 1 were produced using the micro:bit Fritzing diagram available at  https://github.com/microbit-foundation/dev-docs/issues/36  thanks to David Whale ( @whalleygeek  ) for this. The Inputs neurons Neuron 1: from microbit import * W=[-1,-1,1] while True:     x1=pin0.read_digital()     x2=pin1.read_digital()     net = W[0]+W[1]*x1+W[2]*x2 ...