In this post, I will talk about the completed unit on the drone side and show a demo of the remote operated laser pointer in action. The development continues on from the second part of this series.
Here are some pictures of the unit I created. This was mounted on a single lego brick and then attached to the drone via a PGYTECH Tello Adapter.
The whole unit is constructed over a yellow lego brick. I used a 3mm black acrylic sheet and some industrial glue to shape the structure. There is also the use of a cylindrical acrylic rod to support the structure. I learnt that I needed more power to support the RC relay and Laser diode simultaneously. The CR2032/CR2025 3V Vertical Mounting Coin Battery Holders proved an excellent option giving me a total of 12 V. These were stuck one over the other with some industrial glue. The bottom two cells are wired to drive the RC relay circuit. I placed a slider switch for power off/on function. If connected directly to the power supply, the RC relay draws up a little current for powering the signal reception circuit.
The top two coin batteries (CR2032), provide 6V DC supply to the Red laser diode. I put in a 100 Ohm resistor to protect the diode. You’ll also notice that I swapped my previous red laser for one of these.
Here is the demo of the unit in action!
Now, that the Drone end has been taken care of, I will next focus on the processing end. This is the Raspberry Pi and the Intel Neural compute stick 2. What I hope to achieve is to stream the video feed from the Tello Drone to the Raspberry Pi, that also controls the flight of the drone via Python code. Do inferences on the stream and detect target images. If found, then light up the laser pointer on the physical image. I’ll show how in my next post or two.
Here is a closer look at both the transmitter and receiver. I’ve included some pictures of the internals of the transmitter unit as well. We are only temporarily going to use it, as I will explain further. The unit is tiny and light and just what I was looking for related with this project.
Next, I connected all the pieces together so that I could trigger a transmit event that lights up a test green LED and another to switch it off. I haven’t used the red laser diode for testing in this case, as I need to work on a compact circuit that would be able to power both the RC relay and the laser diode. More to come in my next blog post on this.
The distance between the Transmission assembly and the Reception assembly could be an amazing 160m in ideal conditions as this youtube video shows. Here is another video with a successful range test of over 350m with some modifications to the hardware, such as adding longer antennae. The Reception assembly has two circuits with their own power sources. One circuit powers the wireless remote control switch and the other powers the green LED.
The pypi python site has a project for sending and receiving 433/315MHz LPD/SRD signals with generic low-cost GPIO RF modules on a Raspberry Pi. There are two script files that are of use. Click on the links to go to the source code that’s written in python.
Finally, as usual, I’ve recorded a demo of this project in action. Here’s the video:
Now with the RC relay, I can switch the LED on/off from python code. Once I sort out the power circuit issue, it will be possible to ‘build’ the final assembly of the remote controlled laser pointer. I’ll then mount this assembly on my RYZE tello drone. Then will do some tests on that. The most important test would be to check for the stability of the flight of the aircraft with the final assembly mounted on it. And of course, to check whether the laser pointer lights up while the drone is in flight.
Once these basic tests pass, it would be time to focus on capturing live video from the RYZE tello drone onto the Raspberry Pi and doing some object inference / detection using the Intel Movidius Neural compute stick 2 for deep learning . Based on finding certain objects, I would have the code switch the laser that’s mounted on the drone to ON status followed by off. This is really where the fun begins.
I will demonstrate this in action in my next set of posts in this series. Stay tuned!
There are some cool python libraries and machine learning platforms out there to support work in data science. I thought of listing out some of these in this article along with a few highlights of their core capabilities. Also included the links to details for quick reference. Here they are in random order:
statsmodel – Great for statistics around ordinary least squares (OLS), that gives measures such as R-squared, skewness, kurtosis, AIC and BIC scores on the data. It is great for conducting statistical tests, and statistical data exploration.
bokeh – This library is great for providing end users interactive data visualisation inside modern web browsers. Using bokeh, one can quickly and easily make interactive plots, dashboards, and data applications. It can generate highly customisable glyphs such as line, step lines, multiple lines, stacked lines, as well as stacked bars, hex tiles and timeseries plots.
seaborn – Seaborn is a Python data visualization library based on matplotlib. It supports several types of data visualization such as box plot, heatmap, scatterplot, cluster map, and violin plot.
Theano – It is used to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It does this by making use of GPUs for computation. It works with expressions that are passed to it from Python.
yellowbrick – Yellowbrick extends the Scikit-Learn API to make model selection and hyperparameter tuning easier. It builds on top of matplotlib. Some of its key visualization features cover feature, classification, regression, clustering, model selection, target, and text visualizations.
plotly – It is an interactive, open-source, and browser-based graphing library for Python. It supports: basic charts – scatter, line, pie, bar and more statistical charts – histograms, box plots, distplots… scientific charts – contour, heatmaps, ternary plots… financial charts – time series, candlestick, funnel chart… maps, 3D charts, and subplots. It even supports animations.
Keras – A python deep learning library. It supports CNNs and RNNs and can run seamlessly on CPUs and GPUs. It supports the sequential model and Model class that’s used with Keras functional API.
Scikit-learn – A sort of swiss-knife of libraries that allows to perform many objectives not limited to – Classification, Regression, Clustering, Dimensionality reduction, Preprocessing (Transformers and Pipelines) and for model selection.
Numpy – It is the fundamental package for scientific computing in Python. It is great for working with arrays and performing linear algebra operations on arrays. The broadcasting feature is extremely useful, making coding simpler.
Pandas – It is a fast, powerful, flexible and easy to use open-source data analysis and manipulation tool, built on top of the Python programming language. It can import and export data from and to a variety of file formats, such as csv and excel. It can be used to slice the data, subset it, merge/join/concatenate data from multiple sources, and remediate missing data. Pandas supports groupby, pivot table, time series, and sparse datasets. It is one of the most essential tools for a data scientist, especially when needing to perform exploratory data analysis (EDA)
MXNet – A flexible and efficient library for deep learning from Apache. It supports multi GPU and multi host training. MXNet has deep integration into Python and support for Scala, Julia, Clojure, Java, C++, R and Perl.
PaddlePaddle – Is a popular deep learning framework that has been developed and is used in China. PaddlePaddle is originated from industrial practices with dedication and commitments to industrialisation. It has been widely adopted by a wide range of sectors including manufacturing, agriculture, enterprise service and so on while serving more than 1.5 million developers.
Platforms, Ecosystems and Frameworks
TensorFlow – TensorFlow is an end-to-end open-source platform for machine learning. It has all the tools, libraries and resources to build and deploy machine learning-powered applications. Models built with TensorFlow can be deployed on desktop, mobile, web and cloud. This is an offering from Google.
Caffe – A deep learning framework made with expression, speed, and modularity. There is no hardcoding and models and optimization are defined through configuration. Its speed makes it perfect for research experiments and industry deployment. Caffe can be used to create and train CNN inference models.
PyTorch – An open source machine learning framework that accelerates the path from research prototyping to production deployment. It provides an ecosystem of tools and libraries and deployment options deployment to cloud platforms such as Alibaba Cloud, Amazon Web Services (AWS), Google Cloud Platform (GCP) and Microsoft Azure.
Scipy – Python-based ecosystem of open-source software for mathematics, science, and engineering. The SciPy ecosystem includes general and specialized tools for data management and computation, productive experimentation, and high-performance computing. Numpy, Matplotlib, and Pandas are some of the libraries that are part of the Scipy ecosystem.
CNTK – This is a Microsoft offering called Cognitive Toolkit that is open source. CNTK is also one of the first deep-learning toolkits to support the Open Neural Network Exchange ONNX format, an open-source shared model representation for framework interoperability and shared optimization. Co-developed by Microsoft and supported by many others, ONNX allows developers to move models between frameworks such as CNTK, Caffe2, MXNet, and PyTorch.
These were some of the libraries, ecosystems, and platforms that support a lot of work that can be done using them. Spending time exploring and getting experience with these can help make one proficient in the field.