diff options
| author | UnavailableDev <69792062+UnavailableDev@users.noreply.github.com> | 2023-05-25 21:07:27 +0200 |
|---|---|---|
| committer | UnavailableDev <69792062+UnavailableDev@users.noreply.github.com> | 2023-05-25 21:07:27 +0200 |
| commit | fbe109a12420033c3421733072d36a875e154f64 (patch) | |
| tree | 3f0efe7cee27c2389d057f411261fd037fccbb2b | |
| parent | 76df689d48df0b5056769b9c8ca968ac4a0eb261 (diff) | |
| parent | 6daf8036c1342899196b2f7830ae5a18f0918d07 (diff) | |
Merge branch 'master' of https://github.com/unavailabledev/avans-dui
108 files changed, 927 insertions, 288 deletions
diff --git a/.editorconfig b/.editorconfig new file mode 100644 index 0000000..a9383e8 --- /dev/null +++ b/.editorconfig @@ -0,0 +1,11 @@ +root = true + +[*] +indent_style = tab +indent_size = 4 +end_of_line = lf +insert_final_newline = true + +[*.md] +indent_style = space +indent_size = 2 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..3f4b7ab --- /dev/null +++ b/.gitignore @@ -0,0 +1,4 @@ +assets/LSD_straightLines_Pic_0.png +assets/LSD_straightLines_Pic_1.png +assets/hough_straightLines_Pic_0.png +assets/hough_straightLines_Pic_1.png diff --git a/.gitmodules b/.gitmodules deleted file mode 100644 index a60d698..0000000 --- a/.gitmodules +++ /dev/null @@ -1,27 +0,0 @@ -[submodule "zumo/lib/zumo-32u4-arduino-library"] - path = zumo/lib/zumo-32u4-arduino-library - url = https://github.com/pololu/zumo-32u4-arduino-library -[submodule "zumo/lib/fastgpio-arduino"] - path = zumo/lib/fastgpio-arduino - url = https://github.com/pololu/fastgpio-arduino -[submodule "zumo/lib/pushbutton-arduino"] - path = zumo/lib/pushbutton-arduino - url = https://github.com/pololu/pushbutton-arduino -[submodule "zumo/lib/ArduinoCore-avr"] - path = zumo/lib/ArduinoCore-avr - url = https://github.com/arduino/ArduinoCore-avr -[submodule "zumo/lib/usb-pause-arduino"] - path = zumo/lib/usb-pause-arduino - url = https://github.com/pololu/usb-pause-arduino -[submodule "zumo/lib/pololu-buzzer-arduino"] - path = zumo/lib/pololu-buzzer-arduino - url = https://github.com/pololu/pololu-buzzer-arduino -[submodule "zumo/lib/pololu-hd44780-arduino"] - path = zumo/lib/pololu-hd44780-arduino - url = https://github.com/pololu/pololu-hd44780-arduino -[submodule "zumo/lib/pololu-menu-arduino"] - path = zumo/lib/pololu-menu-arduino - url = https://github.com/pololu/pololu-menu-arduino -[submodule "zumo/lib/pololu-oled-arduino"] - path = zumo/lib/pololu-oled-arduino - url = https://github.com/pololu/pololu-oled-arduino diff --git a/RealTime_pictures/rtCorner.class/00000.jpg 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@@ -0,0 +1,72 @@ +\documentclass[11pt, a4paper, english]{article} +\usepackage[margin=1in]{geometry} + +\usepackage{float} +\usepackage{babel} +\usepackage{siunitx} +\usepackage{amsmath} +\usepackage{csquotes} +\usepackage{unicode-math} +\usepackage{fontspec} +\usepackage{tabularx} +\usepackage{booktabs} +\usepackage{needspace} +\usepackage{hyperref} +\usepackage{graphicx} +\usepackage[toc]{glossaries} +\usepackage[backend=biber, + bibencoding=utf8, + style=apa +]{biblatex} +\addbibresource{refs.bib} + +\setmainfont{TeX Gyre Schola} +\setmathfont{TeX Gyre Schola Math} +\sisetup{ + group-separator = {.}, + output-decimal-marker = {,} +} + +\bigskipamount=7mm +\medskipamount=4mm +% \parindent=0mm +\parskip=1ex + +\def\tightlist{} + +\title{\doctitle} +\author{ + Bjorn Martens\\2187272 + \and + Joshua Regnier\\2183008 + \and + Loek Le Blansch\\2180996 + \and + Niels Stunnebrink\\2184532 +} + +\newcommand{\req}[1]{$^{\text{\ref{req:#1}}}$} +\newcommand{\up}[1]{$^{\text{#1}}$} +\newcommand{\sub}[1]{$_{\text{#1}}$} + +\floatplacement{figure}{H} +\floatplacement{table}{H} + +\begin{document} +\begin{titlepage} +\maketitle +\thispagestyle{empty} +\end{titlepage} + +\tableofcontents +\newpage + +\input{\jobname.md.tex} + +% \printbibliography[heading=bibintoc] +% \printglossaries +% \listoftables +% \listoffigures + +\end{document} + diff --git a/doc/dui.md b/doc/dui.md new file mode 100644 index 0000000..cbe99e9 --- /dev/null +++ b/doc/dui.md @@ -0,0 +1,523 @@ +# Introduction + +\<inleiding iemand?\> + +# Problem statement + +The following is the original project description (translated to English). +References have been added in superscript that link to requirements set in +section \ref{specifications}. + +> I would like to bring to market a vehicle that can drive +> independently\req{autonomous} from A to B. The vehicle must take into account +> traffic rules\req{traffic-rules}, road signs\req{signs}, traffic +> lights\req{traffic-lights}, etc. Research is being conducted using a small +> cart, the Pololu Zumo 32U4\req{zumo}, on which a camera module Nicla +> Vision\req{nicla} is mounted. The aim is to investigate the most appropriate +> method of recognizing the road, traffic signs and traffic lights. This should +> be demonstrated with a proof of concept. The cart does not need to drive +> fast\req{drspeed}, so the image processing\req{mvision} does not need to be +> very fast. Assume one frame per second (or faster)\req{imspeed}. + +# Specifications + +The following is a list of specifications derived from the original project +description in section \ref{problem-statement}. + +\begin{enumerate} + \item \label{req:autonomous} + The vehicle is autonomous + \item + The vehicle can detect how its positioned and orientated relative to a road + \item \label{req:traffic-rules} + The vehicle conforms to the following set of traffic rules + \begin{enumerate} + \item Driving when not on a road is not allowed + \item The vehicle can follow a road by steering itself accordingly + \item Driving off the road is only allowed when necessary for the camera to + keep seeing the road + \end{enumerate} + \item \label{req:traffic-lights} + The vehicle handles traffic lights in the following way + \begin{enumerate} + \item Stop at a red traffic light + \item Speed up at an orange traffic light + \item Continue driving normally at a green traffic light + \end{enumerate} + \item \label{req:signs} + The vehicle acts on traffic signs in the following way + \begin{enumerate} + \item Stop at a stop sign, and continue driving after a few seconds + \item Turn left at a left sign + \item Turn right at a right sign + \item Slow down at a low speed limit sign + \item Speed up to normal speed at a high speed limit sign + \end{enumerate} + \item \label{req:zumo} + The vehicle used is a Pololu Zumo 32U4 + \item \label{req:nicla} + The camera module used is an Arduino Nicla Vision + \item \label{req:mvision} + Computer vision / image processing is used as the only input + \item \label{req:drspeed} + There is no minimum speed the car has to drive at + \item \label{req:imspeed} + The image processing pipeline runs at 1 frame per second or higher + \item + The Zumo displays the name of the last detected sign on it's OLED display +\end{enumerate} + +# Architecture + +## Overview + + + +Figure \ref{fig:architecture-level-0} shows the hardware used in this project. +Both the Pololu Zumo 32U4 (referred to as just "Zumo"), and the Arduino Nicla +Vision ("Nicla") have additional sensors and/or outputs on-board, but are +unused. + +## Distribution of features + +Because creating a software architecture that does all machine vision-related +tasks on the Nicla, and all driving related tasks on the Zumo would create +significant overhead, and because the microcontroller on the Zumo is +significantly harder to debug than the Nicla, a monolithic architecture was +chosen. In this architecture, both the detection of 'traffic objects' and the +decisionmaking on how to handle each object is done on the Nicla board. + +Figure \ref{fig:architecture-level-0} shows that a bidirectional communication +line exists between the Zumo and Nicla. This line is only used to send control +commands to the Zumo. Section \ref{niclazumo-communication-protocol} describes +which commands are sent over these lines. + +## Nicla/Zumo communication protocol + +The communication protocol used to control the Zumo from the Nicla uses UART to +send ranged numbers in a single byte. Table \ref{tab:protocol-ranges} shows +which number ranges correspond to which controls. + +\begin{table} +\centering +\begin{tabular}{rl} +\toprule +\textbf{Description} & \textbf{Range (inclusive)}\\ +\midrule +(unused) & \texttt{0x00}\\ +Signs & \texttt{0x01} - \texttt{0x0f}\\ +Speed & \texttt{0x10} - \texttt{0x1f}\\ +Steering & \texttt{0x20} - \texttt{0xff}\\ +\bottomrule +\end{tabular} +\caption{Protocol command ranges} +\label{tab:protocol-ranges} +\end{table} + +### Signs + +The Zumo stores the last sign received, and displays it's name on the OLED +display using the lookup table in table \ref{tab:protocol-signs}. The sign ID +is calculated by subtracting the start offset of the sign command range from +the command as shown in table \ref{tab:protocol-ranges}. + +\begin{table} +\centering +\begin{tabular}{ll} +\toprule +\textbf{ID} & \textbf{Name}\\ +\midrule +\texttt{0x00} & (clear sign)\\ +\texttt{0x01} & Stop sign\\ +\texttt{0x02} & Turn left\\ +\texttt{0x03} & Turn right\\ +\texttt{0x04} & Low speed limit\\ +\texttt{0x05} & High speed limit\\ +\texttt{0x06} & Traffic light (red)\\ +\texttt{0x07} & Traffic light (orange)\\ +\texttt{0x08} & Traffic light (green)\\ +\bottomrule +\end{tabular} +\caption{Sign lookup table} +\label{tab:protocol-signs} +\end{table} + +### Speed + +The speed value ranges from \num{0} to \num{1}, and is converted from the +command using the following formula: + +$$ v(n) = \frac{n - 16}{15} $$ + +### Steering + +The steering value is similar to the speed value, but ranges from \num{-1} +(left) to \num{1} (right). The zumo has a built in "influence" value, which +limits the smallest radius the robot can turn at. The steering value is +converted using the following formula: + +$$ s(n) = \frac{n - 32}{223}\cdot2-1 $$ + +## Zumo internal motor control functions + +The Zumo robot receives a speed and steering value. Because the protocol has a +limited precision due to the low amount of data sent, the following formula is +used to control motor speeds $M_1$ and $M_2$ from steering value $s$ and speed +value $v$. The constant $C_1$ is used to globally limit the speed the robot can +drive at. $C_2$ represents the amount of influence the steering value has on +the corner radius, where \num{0} is no steering at all and \num{1} completely +turns of one motor when steering fully left or right: + +$$ M_{1,2} = \frac{v(\pm s C_2 - C_2 + 2)}{2} C_1 $$ + +By default, $C_1 = \num{96}$ and $C_2 = \num{0.6}$ + +The Zumo firmware also smooths incoming values for $s$ and $v$ using a PID +controller. The default constants for the PID controller used are: + +\begin{align*} +K_p &= -0.02\\ +K_i &= +0.13\\ +K_d &= -300.0 +\end{align*} + +# Research + +## Detecting lines + +The Zumo robot needs to drive in a road map-like environment where it needs to act like a car. With the nicla vision camera, there needs to be a way for detecting lines in every frame to make the Zumo robot ride between the lines. Read lines from an image there are different algorithms to make it work. We need to make sure that it works on the OpenMV program if we only choose this one. In this research, two techniques are researched: convolution-based and feature-based. + +### Different line detection algorithms. + +#### Hough Transform + +This is a popular algorithm used to detect straight lines in an image. It works by transforming the image from Cartesian space to Hough space, where lines are represented as points. The algorithm then looks for clusters of points in Hough space, which correspond to lines in Cartesian space. + +For more information about Hough Transform algorithms check the below links: + +- [Wiki hough](https://en.wikipedia.org/wiki/Hough_transform ) +- [Science article](https://www.sciencedirect.com/topics/computer-science/hough-transforms) +- [OpenCV Hough](https://docs.opencv.org/3.4/d9/db0/tutorial_hough_lines.html) +- [OpenMV find_lines](https://docs.openmv.io/library/omv.image.html) + +#### EDlines + +EDLines, short for Edge Drawing Lines, is a feature-based algorithm that detects straight lines in an image by tracing along the edges of the image. It works by first extracting edges from the image, then building a graph where each edge is represented by a node. The algorithm then uses a greedy strategy to connect the nodes with high edge strength to form line segments. Finally, it merges line segments that are collinear and close to each other to form longer lines. This algorithm does not require a parameter search or optimization and is known for its robustness against noise and partial occlusion. + +For more information about EDlines algorithms check the below links: + +- [github library](https://github.com/CihanTopal/ED_Lib) +- [Science article](https://www.sciencedirect.com/science/article/abs/pii/S0167865511001772) +- [EDLINES: REAL-TIME LINE SEGMENT DETECTION BY EDGE DRAWING (ED)](https://projet.liris.cnrs.fr/imagine/pub/proceedings/ICIP-2011/papers/1569406487.pdf) +- [OpenCV EDlines doc](https://docs.opencv.org/3.4/d4/d8b/group__ximgproc__edge__drawing.html) + +#### Line Segment Detector + +LSD (Line Segment Detector) is an algorithm used for detecting line segments in an image. It works by analyzing the gradient information in the image and clustering nearby gradients that form a line segment. The algorithm first computes the gradient information for the image using the Gaussian filter. It then performs a series of operations, such as non-maximum suppression and thresholding, to obtain a binary edge map. + +The line segments are detected by applying a series of geometric constraints to the edge map. These constraints include the minimum and maximum length of line segments, the minimum angle between line segments, and the maximum deviation of line segments from a straight line. + +Once the line segments are detected, they are refined using a line merging algorithm combining nearby line segments into longer, more continuous lines. The resulting line segments and their endpoints are returned as the output of the algorithm. + +For more information about Line Segment Detector algorithms check the below links: + +- [LSD: a Line Segment Detector pdf](http://www.ipol.im/pub/art/2012/gjmr-lsd/article.pdf) +- [Working behind LSD](https://saiwa.ai/blog/line-segment-detection-2/) +- [OpenCV LSD doc](https://docs.opencv.org/3.4/db/d73/classcv_1_1LineSegmentDetector.html) +- [OpenMV find_line_segments](https://docs.openmv.io/library/omv.image.html) + +#### Radon transform + +Radon transform is another popular algorithm used for line detection. It works by computing the line integral of an image along different directions. The algorithm rotates the image at different angles and computes the sum of pixel intensities along each line in the image. The result is a two-dimensional matrix called the Radon transform. Peaks in this matrix correspond to the lines in the original image. The algorithm then applies some post-processing steps to identify and extract the lines from the Radon transform. + +For more information about Radon transform algorithms check the below links: + +- [Science article](https://www.sciencedirect.com/science/article/abs/pii/0031320396000155) +- [matlab Radon](https://stackoverflow.com/questions/35412573/radon-transform-line-detection) +- [Matlab elaboration Radon](https://www.kevinpolisano.com/Doctorat/doc-matlab-exemple/radon_lines_detection.html) +- [OpenCV Radon doc](https://docs.opencv.org/4.x/d5/d89/radon__transform_8hpp.html) + +### Which algorithm is suitable for our project? + +We have identified four different types of line detection algorithms that could potentially be used for our project. To decide on the best algorithm, we need to consider various factors such as accuracy, efficiency, and ease of use. While processing time is not a critical factor in our case, we need to ensure that the algorithm we choose meets our requirements and is accessible through the platform we are using, which is currently openMV but may change to openCV in the future. Therefore, our priority is to select an algorithm that is easy to implement, provides accurate results, and is compatible with our platform. + +#### OpenMV + +The only two algorithms that work with OpenMV are Hough Transform, the function find_lines, and Line Segment Detector, also known as find_line_segments. Both of these have their ups and downs and could be used for our project. find_lines has the most ups whereas find_line_segemtns has the most negative. As the result here below is decently optimized, it is first grayscaled, and then canny edge detection is done to it. + +For the test are the straight lines pictures used with different lighting additionality the left lane represents a whitish line and the right lane is drawn with a more darker color. here below are the pictures used: + + + + + +##### find_lines + +The find_lines is a very fast function where you can handle straight lines and other lines with at least 45 FPS or more. Also, have a lot of control over the different types of parameters. + +This is the outcome of picture 1: + + +This is the outcome of picture 2: + + +As you can see there isn't much of a difference between the two pictures. + +##### find_line_segments + +The find_line_segments is a very slow function where you can find segments from a line. This is a easier to use function because it only has two parameters but the frame rate drops significantly. Additionally, the size of the image to run the algorithm on needs to be smaller because of memory. + +This is the outcome of picture 1: + + + +This is the outcome of picture 2: + + + +As you can see there is quite a lot of difference between them. This function needs more refinement but I couldn't find the sweet spot. Also, the right line in different pictures was always the problem, so there needs another solution for this function to work better. + +#### OpenCV + +All the above algorithms could be used with OpenCV, But the Radon transform needs more work than the others with the amount of information in the doc. + +## Communication between the Nicla and Zumo + +In order to make the Zumo robot both detect where it is on a road, and steer to +keep driving on said road, some sort of communication needs to exist between +the Nicla and Zumo. As mentioned earlier\footnote{dit is nog niet benoemd}, all +machine vision-related tasks will happen on the Nicla board. Because the Nicla +board is the first to know how much to steer the cart, it makes sense to have +it control the cart by giving the Nicla a 'steering wheel' of sorts. + +This section tries to answer the question "What is the best protocol to use +over the existing UART connection between the Nicla and Zumo?". After a +brainstorm session, we came up with the following specifications for the +communication protocol: + +1. **Low bandwidth** + Less data means more responsive steering +2. **As simple as possible** + The Nicla only needs to control speed and steering +3. **Easy to mock and test** + The cart should be able to be controlled using a mock driver and the Nicla's + output should be testable (preferably using unit tests) +4. **Adaptive to noisy data** + The cart should gradually change speed and steering direction as to not slip + or cause excessive motion blur for the camera module on the Nicla +5. **Adaptive to Nicla failure** + If the Nicla crashes or can't detect anything, it will stop sending control + commands. In this case, the Zumo robot should slowly come to a halt. + +Where possible, it's generally benificial to re-use existing code to save on +time. Existing code exists for a custom binary protocol and a text-based +command protocol. Both of these were designed without bandwidth or latency in +mind, and mostly focus on robustness in the case of temporary disconnects or +noise on the communication lines, so a new protocol needs to be made. + +To address specification 1 and 2, the command length is fixed at 1 byte. This +means that UARTs built-in start/stop bit will take care of message start/end +detection, since most software interfaces for UART (including Arduino) string +multiple sequential messages together even if they're not part of the same UART +packet. + +To mock messages from the Nicla to the Zumo robot, a simple USB serial to UART +cable can be used, along with a small C or Python program to convert +keyboard/mouse input into steering/speed commands. A small software layer can +be implemented on the Nicla to log the semantic meaning of the commands instead +of sending actual UART data when run in a unit test. + +A PID controller can be used to smoothly interpolate between input +speed/steering values. This would also introduce some lag between when the +Nicla knows how much to steer, and when the Zumo actually steered the wanted +amount. Smoothing the speed/steering values does make it virtually impossible +for the Nicla to make it's own input data unusable because of motion blur, so +the lag needs to be handled in some other way as directly controlling speed +values without interpolation would lead to a garbage-in-garbage-out system. The +simplest solution to motion blur is limiting the maximum speed the Zumo robot +can drive at, which is the solution we're going to use as speed is not one of +the criteria of the complete system\footnote{Problem statement +(section \ref{problem-statement})}. + +In the case the Nicla module crashes or fails to detect the road or roadsigns, +it will stop sending commands. If the Zumo robot would naively continue at it's +current speed, it could drive itself into nearby walls, shoes, pets, etc. To +make sure the robot doesn't get 'lost', it needs to slow down once it hasn't +received commands for some time. As mentioned in section \ref{TODO}, the Nicla +module is able to process at about 10 frames per second, so 2 seconds is a +reasonable time-out period. + +\def\communicationConclusion{ +The complete protocol consists of single byte commands. A byte can either +change the cart speed or steering direction, both will apply gradually. When no +commands have been received for more than 2 seconds, the Zumo robot will +gradually slow down until it is stopped. Exact specifications of commands are +provided in the protocol specification document\footnote{dit document bestaat +nog niet}. +} +\communicationConclusion + +## Compiling and linking code for the Zumo + +This section tries to answer the question "What are possible debugging options +for code running on the Zumo robot?". + +Debugging running code can usually be done using an on-device debugger, and a +program that interfaces with the debugger and gcc on a computer. Because gcc +only gives valuable information when it has access to the executable file +running on the microcontroller, an attempt at making a makefile-based toolchain +was made. + +Pololu provides C++ libraries for controlling various parts of the Zumo's +hardware. These libraries make use of functions and classes that are part of +the Arduino core library. The Arduino libraries are all open source, and can in +theory be compiled and linked using make, but this would take more effort than +it's worth, since the Zumo has very little responsibility in the end product. + +\def\buildSystemConclusion{ +Because making a custom build system would take too much time, and because the +Zumo robot's code is very simple, unit tests are used to debug the Zumo's code. +For compiling and uploading the full Zumo firmware, the Arduino IDE is used in +combination with the standard Pololu boards and Libraries. +} +\buildSystemConclusion + +## Traffic Sign Detection (TSD) + +The following chapters will look at the different methods used to detect signs in order to find a suitable solution for this project. + +### Color based + +The distinct color characteristics of traffic signs can attract drivers’ attention and can also provide important cues to design color based detection methods. In the past decades, a large amount of detection methods are designed to detect distinct traffic sign colors such as blue, red and yellow. These methods can be directly used for traffic sign detection, and can also be used for preliminary reduction of the search space, followed by other detection methods + +#### RGB + +One can easily look at the RGB values to detect a certain color. Although the r,g and b values are heavily effected by different illuminations, therefore this isn't a reliable solution in variating lighting conditions. + +An example implementation: + +```py +#Red +if(R >= ThR and G <= thG) + +#Blue +if(B >= thB) + +#Yellow +if((R + G) >= ThY) +``` + +It is possible to enhance the colors with maximum and minimum operations: + +```py +fR(i) = max(0, min(iR − iG, iR − iB)/s), +fB(i) = max(0, min(iB − iR, iB − iG)/s), +fY(i) = max(0, min(iR − iB, iG − iB)/s). +``` + +This method can result in some issues on the blue channel (see source 1 page 86583 for more explanation). As a solution to this issue use the following formula for the blue channel instead: +```py +f′B(i) = max((0, iB − iR)/s). +``` + +#### HSV + +The HSV/HSI color space is more immune to the illumination challenges of RGB. The hue and saturation channels can be calculated using RGB, which increases the processing time. +The following pseudo code shows how to detect the red, blue and yellow colors in this space. + +```python +#Red +if (H <= Th1 or + H >= Th2) + +#Blue +if (H >= Th1 and + H <= Th2) + +#Yellow + +if (H >= Th1 and + H <= Th2 and + H <= Th3) +``` + +#### LAB + +This color space is used for finding uncorrelated color components, the L\*a\*b\* space was used for detecting blue, red, yellow and green colors. + +#### Pixel classification + +This method avoids the use of fixed thresholds that might need adjusting at times. In order to resolve this some authors tried to transfer the problem into pixel classification where a neural network classifies every pixel in the input image, the pixel classification algorithms are often slower than other color extraction methods. + +#### results + +\def\signDetectionColor{ +The above described methods where also applied to a database in order to compare each method. This resulted in the conclusion that, using a normalized RGB space is giving the a mixture of most detections and least false-positve results. See source 1 page 86584 for the full report. +} +\signDetectionColor + +### Shape based + +Common standard shapes of traffic signs are triangle, circle, rectangle, and octagon. Shape characteristics used for shape detection include standard shapes, boundaries, texture, key points, etc. + +#### Hough + +See line detection hough. +<!-- TODO: not enough info found --> + +#### Barnes *Fang et al* (fast radial symmetry) + +Seemingly a 'simpler' method +<!-- TODO: (currently no great references found) --> + +#### Fourier +This method also deals with occlusion and morphing/rotating the shape flat again (when looking at it at an angle). + +#### Key points detection +This, simply put, looks at the edges/corners in order to find an ROI. + +### Color & Shape based +In some methods, the shape detection methods can be combined with color based methods to fulfill the traffic sign detection work. +For example, using a color based detection to find a ROI and using shape detection in that ROI to determine if it is a sign. + +### Neural networks +<!-- TODO: --> + +### results + +\def\signDetectionShape{ +Most shape based recognition methods are more complex than using a color based detection. But the method 'Fourier' seems the most useful as it can also deal with rotated and occluded objects. +} +\signDetectionShape + +## Traffic Sign Recognition (TSR) +After traffic sign detection or tracking, traffic sign recognition is performed to classify the detected traffic signs into correct classes. + + + +### Binary tree +The binary-tree-based classification method usually classify traffic signs according to the shapes and colors in a coarse-to-fine tree process. + +### Support Vector Machine (SVM) +As a binary-classification method, SVM classifies traffic signs using one-vs-one or one-vs-others classification process. + +## results + +\def\signRecognition{ +While making a binary tree is seemingly the most simple, yet effective solution. Using the 'Fourier' method is a bit more complex, it may be a better solution (this requires testing). +} +\signRecognition + +# Conclusion + +\communicationConclusion +\buildSystemConclusion +\signDetectionColor +\signDetectionShape +\signRecognition + +## Sources: + +1. [IEEE, Digital Object Identifier June 26, 2019 (pages 86578 - 86596)](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8746141) + diff --git a/doc/dui.tex b/doc/dui.tex new file mode 100644 index 0000000..612ee40 --- /dev/null +++ b/doc/dui.tex @@ -0,0 +1,3 @@ +\newcommand\doctitle{DUI} +\input{base.tex} + diff --git a/doc/makefile b/doc/makefile new file mode 100644 index 0000000..3446eeb --- /dev/null +++ b/doc/makefile @@ -0,0 +1,26 @@ +.PHONY: all clean + +all: dui.pdf + +dui.pdf: ../assets/architecture-level-0.pdf +dui.pdf: ../assets/LSD_straightLines_Pic_0.png +dui.pdf: ../assets/LSD_straightLines_Pic_1.png +dui.pdf: ../assets/hough_straightLines_Pic_0.png +dui.pdf: ../assets/hough_straightLines_Pic_1.png + +%.png: %.bmp + convert $< $@ + +%.pdf: %.svg + rsvg-convert -f pdf -o $@ $< + +%.pdf: %.tex base.tex %.md.tex + latexmk $< -shell-escape -halt-on-error -lualatex -f -g + +%.md.tex: %.md + pandoc -t latex -o $@ $< + +clean: + $(RM) dui.aux dui.bbl dui.bcf dui.blg dui.fdb_latexmk dui.fls dui.log dui.out dui.run.xml dui.synctex.gz dui.toc dui.md.tex dui.pdf + $(RM) ../assets/LSD_straightLines_Pic_0.png ../assets/LSD_straightLines_Pic_1.png ../assets/hough_straightLines_Pic_0.png ../assets/hough_straightLines_Pic_1.png + diff --git a/doc/refs.bib b/doc/refs.bib new file mode 100644 index 0000000..e69de29 --- /dev/null +++ b/doc/refs.bib diff --git a/docs/research_sign_detect.md b/docs/research_sign_detect.md deleted file mode 100644 index b81ab38..0000000 --- a/docs/research_sign_detect.md +++ /dev/null @@ -1,130 +0,0 @@ - -# List of abriviations - -- ROI (aRea Of Interest) - - - -# The traffic sign problem: - -<!-- We divide the reviewed detection methods into five main categories: color-based methods, shape-based methods, color- and shape-based methods, machine-learning-based methods, and LIDAR-based methods --> - -# Traffic Sign Detection (TSD) - -## Color based - -The distinct color characteristics of traffic signs can attract drivers’ attention and can also provide important cues to design color based detection methods. In the past decades, a large amount of detection methods are designed to detect distinct traffic sign colors such as blue, red and yellow. These methods can be directly used for traffic sign detection, and can also be used for preliminary reduction of the search space, followed by other detection methods - -### RGB - -One can easily look at the RGB values to detect a certain color. Although the r,g and b values are heavily effected by different illuminations, therefore this isn't a reliable solution in variating lighting conditions. - -An example implementation: - -```py -#Red -if(R >= ThR and G <= thG) - -#Blue -if(B >= thB) - -#Yellow -if((R + G) >= ThY) - -``` - -It is possible to enhance the colors with maximum and minimum operations: - -``` -fR(i) = max(0, min(iR − iG, iR − iB)/s), -fB(i) = max(0, min(iB − iR, iB − iG)/s), -fY(i) = max(0, min(iR − iB, iG − iB)/s). -``` - -This method can result in some issues on the blue channel (see source 1 page 86583 for more explanation). As a solution to this issue use the following formula for the blue channel instead: -``` -f′B(i) = max(0, iB − iR)/s). -``` - -### HSV - -The HSV/HSI color space is more immune to the illumination challenges of RGB. The hue and saturation channels can be calculated using RGB, which increases the processing time. - -The following pseudo code shows how to detect the red, blue and yellow colors in this space. - -```python -#Red -if (H <= Th1 or - H >= Th2) - -#Blue -if (H >= Th1 and - H <= Th2) - -#Yellow - -if (H >= Th1 and - H <= Th2 and - H <= Th3) - -``` - -### LAB - -This color space is used for finding uncorrelated color components, the L\*a\*b\* space was used for detecting blue, red, yellow and green colors. - - -### Pixel classification - -This method avoids the use of fixed thresholds that might need adjusting at times. In order to resolve this some authors tried to transfer the problem into pixel classification where a neural network classifies every pixel in the input image, the pixel classification algorithms are often slower than other color extraction methods. - -### results - -The above described methods where also applied to a database in order to compare each method. This resulted in the conclusion that using a normalized RGB space is giving the most accurate results. See source 1 page 86584 for the full report. - -## Shape based - -Common standard shapes of traffic signs are triangle, circle, rectangle, and octagon. Shape characteristics used for shape detection include standard shapes, boundaries, texture, key points, etc. - -### Hough -<!-- TODO: no info found --> - - -### Barnes *Fang et al* (fast radial symmetry) -Seemingly a 'simpler' method -<!-- TODO: (currently no great references found) --> - - -### Fourier -This method also deals with occlusion and morphing the shape flat again (when looking at it at an angle). - - -### Key points detection -This, simply put, looks at the edges/corners in order to find an ROI. - -## Color & Shape based -In some methods, the shape detection methods can be combined with color based methods to fulfill the traffic sign detection work. - -For example, using a color based detection to find a ROI and using shape detection in that ROI to determine if it is a sign. - - -## Neural networks -<!-- TODO: --> - - -# Traffic Sign Recognition (TSR) -After traffic sign detection or tracking, traffic sign recognition is performed to classify the detected traffic signs into correct classes. - - - -## Binary tree -The binary-tree-based classification method usually classify traffic signs according to the shapes and colors in a coarse-to-fine tree process. - -## Support Vector Machine (SVM) -As a binary-classification method, SVM classifies traffic signs using one-vs-one or one-vs-others classification process. - - - -# Sources: - -1. [IEEE, Digital Object Identifier June 26, 2019 (pages 86578 - 86596)](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8746141)
\ No newline at end of file diff --git a/zumo/.gitignore b/zumo/.gitignore index d3e1535..e45f7a2 100644 --- a/zumo/.gitignore +++ b/zumo/.gitignore @@ -1,5 +1,2 @@ -main *.o -*.hex -compile_commands.json -.cache +pidtest diff --git a/zumo/lib/ArduinoCore-avr b/zumo/lib/ArduinoCore-avr deleted file mode 160000 -Subproject 42fa4a1ea1b1b11d1cc0a60298e529d37f9d14b diff --git a/zumo/lib/fastgpio-arduino b/zumo/lib/fastgpio-arduino deleted file mode 160000 -Subproject 5853c593b07730e632127570279e7dea06de1af diff --git a/zumo/lib/pololu-buzzer-arduino b/zumo/lib/pololu-buzzer-arduino deleted file mode 160000 -Subproject 20ef7b734814a7c5e9f02f54a2cdd41729759d5 diff --git a/zumo/lib/pololu-hd44780-arduino b/zumo/lib/pololu-hd44780-arduino deleted file mode 160000 -Subproject 0a35789a58ca92ca6f69b63fbc269959b2d1858 diff --git a/zumo/lib/pololu-menu-arduino b/zumo/lib/pololu-menu-arduino deleted file mode 160000 -Subproject 8970b8db6e4e80b1c0e95172a87a35410bf593b diff --git a/zumo/lib/pololu-oled-arduino b/zumo/lib/pololu-oled-arduino deleted file mode 160000 -Subproject e6b83b6c181962ffb98f99a4a4c3fd7cbf7e670 diff --git a/zumo/lib/pushbutton-arduino b/zumo/lib/pushbutton-arduino deleted file mode 160000 -Subproject 442884e004859ff550d5c0cae70033d7d568fc2 diff --git a/zumo/lib/usb-pause-arduino b/zumo/lib/usb-pause-arduino deleted file mode 160000 -Subproject 3f76e486460bb351f9738c86595f22db2da34c5 diff --git a/zumo/lib/zumo-32u4-arduino-library b/zumo/lib/zumo-32u4-arduino-library deleted file mode 160000 -Subproject f4dfe054e23176ba445748b4b91f463701e7eb7 diff --git a/zumo/main.cpp b/zumo/main.cpp deleted file mode 100644 index 23d13db..0000000 --- a/zumo/main.cpp +++ /dev/null @@ -1,13 +0,0 @@ -#include <Zumo32U4.h> -#include <Arduino.h> -#include <Wire.h> - -void setup() { -} - -void loop() { - ledRed(1); - delay(1000); - ledRed(0); - delay(1000); -} diff --git a/zumo/makefile b/zumo/makefile deleted file mode 100644 index c388a28..0000000 --- a/zumo/makefile +++ /dev/null @@ -1,49 +0,0 @@ -PORT = /dev/ttyUSB0 - -C++ = avr-g++ -CC = avr-gcc -LD = avr-ld -RM = rm -f - -MCU = atmega32u4 -TARGET = main -SRCS += main.cpp - -CFLAGS += -mcall-prologues -CFLAGS += -mmcu=$(MCU) -CFLAGS += -Os -CFLAGS += -g -CFLAGS += -Wl,-gc-sections -CFLAGS += -Wl,-relax - -include zumo.mk - -OBJS := $(patsubst %.c,%.o, $(SRCS)) -OBJS := $(patsubst %.cpp,%.o, $(OBJS)) - -MAKEFLAGS += -j4 - -.PHONY: all clean flash - -all: $(TARGET).hex - -%.o: %.cpp - $(C++) $(CFLAGS) -o $@ -c $< - -%.o: %.c - $(CC) $(CFLAGS) -o $@ -c $< - -$(TARGET): $(OBJS) - $(LD) $(LFLAGS) -o $@ $^ - -$(TARGET).hex: $(TARGET) - avr-objcopy -R .eeprom -O ihex $< $@ - -flash: $(TARGET).hex - avrdude -p $(MCU) -c avr109 -P $(PORT) -U flash:w:$(TARGET).hex - -clean: - $(RM) $(TARGET) $(TARGET).hex $(OBJS) - -compile_commands.json: makefile - compiledb make -Bn diff --git a/zumo/pid.cpp b/zumo/pid.cpp new file mode 100644 index 0000000..7c1cbda --- /dev/null +++ b/zumo/pid.cpp @@ -0,0 +1,52 @@ +#include "pid.h" + +PID::PID(float P, float I, float D) { + A0 = P + I * dt; + A1 = -P; + A0d = D / dt; + A1d = -2 * D / dt; + A2d = D / dt; + tau = D / (P * N); + alpha = dt / (2 * tau); + reset(0.0); +} + +// https://en.wikipedia.org/wiki/PID_controller#Pseudocode +float PID::iter(float target) { + error[2] = error[1]; + error[1] = error[0]; + error[0] = target - output; + + output = output + A0 * error[0] + A1 * error[1]; + + d1 = d0; + d0 = A0d * error[0] + A1d * error[1] + A2d * error[2]; + fd1 = fd0; + fd0 = ((alpha) / (alpha + 1)) * (d0 + d1) - ((alpha - 1) / (alpha + 1)) * fd1; + output = output + fd0; + + if (output < -1) output = -1; + if (output > 1) output = 1; + return output; +} + +void PID::reset(float value) { + error[0] = 0.0; + error[1] = 0.0; + error[2] = 0.0; + d0 = 0; + d1 = 0; + fd0 = 0; + fd1 = 0; + output = value; +} + +PID speed_pid = PID(); +PID steer_pid = PID(); +PID speed_mod_pid = PID(); +void apply_pid(dui_state_t* target, dui_state_t* current) { + current->speed = speed_pid.iter(target->speed); + current->steer = steer_pid.iter(target->steer); + current->speed_mod = speed_mod_pid.iter(target->speed_mod); +} + diff --git a/zumo/pid.h b/zumo/pid.h new file mode 100644 index 0000000..beb73ac --- /dev/null +++ b/zumo/pid.h @@ -0,0 +1,21 @@ +#pragma once + +#include "protocol.h" + +class PID { +private: + float A0, A1, A0d, A1d, A2d, tau, alpha, d0, d1, fd0, fd1; + float error[3]; + float output; + const float dt = 1.0; + const float N = 10.0; + +public: + PID(float P = -0.02, float I = 0.13, float D = -300.0); + float iter(float target); + void reset(float value); +}; + +/** @brief edit `current` to be closer to `target` using PID controllers */ +void apply_pid(dui_state_t* target, dui_state_t* current); + diff --git a/zumo/pidtest.cpp b/zumo/pidtest.cpp new file mode 100644 index 0000000..b9ce50b --- /dev/null +++ b/zumo/pidtest.cpp @@ -0,0 +1,18 @@ +#include <cstdio> +#include <random> + +#include "pid.h" + +int main() { + float P, I, D; + P = -0.02; + I = 0.13; + D = -300; + PID test(P, I, D); + test.reset(0.0); + + fprintf(stderr, "P: %.3f :: I: %.3f :: D: %.3f\n", P, I, D); + for (unsigned int i = 0; i < 100; i++) { + printf("%2.8f\n", test.iter(i < 50 ? 1.0 : 0.0)); + } +} diff --git a/zumo/pidtest.mk b/zumo/pidtest.mk new file mode 100644 index 0000000..5ffe1e1 --- /dev/null +++ b/zumo/pidtest.mk @@ -0,0 +1,21 @@ +CPP = g++ +LD = g++ +RM = rm -f +CFLAGS = +LFLAGS = +TARGET = pidtest + +SRCS := pidtest.cpp pid.cpp +OBJS := pidtest.o pid.o + +all: pidtest + +%.o: %.cpp + $(CPP) -c $(CFLAGS) $< -o $@ + +$(TARGET): $(OBJS) + $(LD) $^ $(LFLAGS) -o $@ + +clean: + $(RM) $(TARGET) $(OBJS) + diff --git a/zumo/protocol.cpp b/zumo/protocol.cpp new file mode 100644 index 0000000..fea8a00 --- /dev/null +++ b/zumo/protocol.cpp @@ -0,0 +1,39 @@ +#include <Zumo32U4Motors.h> + +#include "protocol.h" + +#define DUI_SPEED_MOD 96.0f +#define DUI_MOTOR_DIFF 0.6f + +#define DUI_CMD_NULL 0x00 +#define DUI_CMD_SIGN_START 0x01 +#define DUI_CMD_SIGN_END 0x0f +#define DUI_CMD_STEER_START 0x10 +#define DUI_CMD_STEER_END 0x1f +#define DUI_CMD_SPEED_START 0x20 +#define DUI_CMD_SPEED_END 0xff + +void handle_cmd(unsigned char cmd, dui_state_t *state) { + if (cmd == DUI_CMD_NULL) return; + else if (DUI_CMD_SIGN_START <= cmd && cmd <= DUI_CMD_SIGN_END) { + state->current_sign = (dui_e_sign) (cmd - DUI_CMD_SIGN_START); + } else if (DUI_CMD_STEER_START <= cmd && cmd <= DUI_CMD_STEER_END) { + state->steer = (float) (cmd - DUI_CMD_STEER_START) / (float) (DUI_CMD_STEER_END - DUI_CMD_STEER_START); + } else if (DUI_CMD_SPEED_START <= cmd && cmd <= DUI_CMD_SPEED_END) { + state->speed = ((float) (cmd - DUI_CMD_SPEED_START) / (float) (DUI_CMD_SPEED_START - DUI_CMD_SPEED_END) * (float) 2 - (float) 1); + } +} + +void apply_state(dui_state_t *state) { + float motor_l = 0.5f * state->speed * (+1.f * state->steer * DUI_MOTOR_DIFF - DUI_MOTOR_DIFF + 2) * state->speed_mod * DUI_SPEED_MOD; + float motor_r = 0.5f * state->speed * (-1.f * state->steer * DUI_MOTOR_DIFF - DUI_MOTOR_DIFF + 2) * state->speed_mod * DUI_SPEED_MOD; + + Zumo32U4Motors::setLeftSpeed((int16_t) motor_l); + Zumo32U4Motors::setRightSpeed((int16_t) motor_r); + + // TODO: print sign on OLED screen +} + +unsigned char uart_read() { + return 0x00; +} diff --git a/zumo/protocol.h b/zumo/protocol.h new file mode 100644 index 0000000..662a5ce --- /dev/null +++ b/zumo/protocol.h @@ -0,0 +1,35 @@ +#pragma once + +typedef enum { + DUI_CMD_NULL, + DUI_CMD_SIGN, + DUI_CMD_SPEED, + DUI_CMD_STEER, +} dui_e_cmd; + +typedef enum { + DUI_SIGN_NONE, /** @brief no sign */ + DUI_SIGN_STOP, /** @brief stop (set speed to 0) */ + DUI_SIGN_LEFT, /** @brief turn left (set steer to -1) */ + DUI_SIGN_RIGHT, /** @brief turn right (set steer to +1) */ + DUI_SIGN_SPEED_LIMIT_LOW, /** @brief slow down (speed limit 0.5) */ + DUI_SIGN_SPEED_LIMIT_HIGH, /** @brief full speed (speed limit 1.0) */ + DUI_SIGN_LIGHT_STOP, /** @brief traffic light red (set speed to 0) */ + DUI_SIGN_LIGHT_FLOOR_IT, /** @brief traffic light orange (set speed to 2 temporarily) */ + DUI_SIGN_LIGHT_GO, /** @brief traffic light green (keep current speed) */ +} dui_e_sign; + +typedef struct { + float steer; /** @brief steer value (-1 is left, +1 is right) */ + float speed; /** @brief speed (0-15) */ + dui_e_sign current_sign; /** @brief last seen sign */ + float speed_mod; /** @brief global speed multiplier */ +} dui_state_t; + +/** @brief non blocking read byte */ +unsigned char uart_read(); +/** @brief read and apply cmd to state */ +void handle_cmd(unsigned char cmd, dui_state_t *state); +/** @brief apply state to motors */ +void apply_state(dui_state_t* state); + diff --git a/zumo/zumo.ino b/zumo/zumo.ino new file mode 100644 index 0000000..f59bd36 --- /dev/null +++ b/zumo/zumo.ino @@ -0,0 +1,34 @@ +#include <Zumo32U4.h> +#include <Arduino.h> +#include <Wire.h> + +#include "protocol.h" +#include "pid.h" + +dui_state_t g_dui_target_state = { + .steer = 1.0f, + .speed = 1.0f, + .current_sign = DUI_SIGN_NONE, + .speed_mod = 1.f, +}; +dui_state_t g_dui_current_state = { + .steer = 0.f, + .speed = 1.f, + .current_sign = DUI_SIGN_NONE, + .speed_mod = 1.f, +}; + +void setup() { +} + +void loop() { + unsigned char cmd = 0x00; + while ((cmd = uart_read())) + handle_cmd(cmd, &g_dui_target_state); + + apply_pid(&g_dui_target_state, &g_dui_current_state); + + apply_state(&g_dui_current_state); + + delay(10); +} diff --git a/zumo/zumo.mk b/zumo/zumo.mk deleted file mode 100644 index 3645513..0000000 --- a/zumo/zumo.mk +++ /dev/null @@ -1,65 +0,0 @@ -CFLAGS += -DF_CPU=16000000 -CFLAGS += -I./lib/zumo-32u4-arduino-library/src -CFLAGS += -I./lib/fastgpio-arduino -CFLAGS += -I./lib/pushbutton-arduino -CFLAGS += -I./lib/usb-pause-arduino -CFLAGS += -I./lib/pololu-buzzer-arduino/src -CFLAGS += -I./lib/pololu-hd44780-arduino -CFLAGS += -I./lib/pololu-menu-arduino/src -CFLAGS += -I./lib/pololu-oled-arduino/src -CFLAGS += -I./lib/ArduinoCore-avr/cores/arduino -CFLAGS += -I./lib/ArduinoCore-avr/libraries/HID/src -CFLAGS += -I./lib/ArduinoCore-avr/libraries/SoftwareSerial/src -CFLAGS += -I./lib/ArduinoCore-avr/libraries/SPI/src -CFLAGS += -I./lib/ArduinoCore-avr/libraries/EEPROM/src -CFLAGS += -I./lib/ArduinoCore-avr/libraries/Wire/src -CFLAGS += -I./lib/ArduinoCore-avr/variants/circuitplay32u4 - -CFLAGS += -L/usr/avr/lib/avr5/ -L/usr/lib/gcc/avr/12.2.0/avr5 -lgcc -lm -lc -latmega32u4 -LFLAGS += -L/usr/avr/lib/avr5/ -L/usr/lib/gcc/avr/12.2.0/avr5 -lgcc -lm -lc -latmega32u4 - -LIBS += lib/ArduinoCore-avr/cores/arduino/PluggableUSB.cpp -LIBS += lib/ArduinoCore-avr/cores/arduino/CDC.cpp -LIBS += lib/ArduinoCore-avr/cores/arduino/HardwareSerial.cpp -LIBS += lib/ArduinoCore-avr/cores/arduino/USBCore.cpp -# LIBS += lib/ArduinoCore-avr/cores/arduino/HardwareSerial0.cpp -# LIBS += lib/ArduinoCore-avr/cores/arduino/HardwareSerial1.cpp -# LIBS += lib/ArduinoCore-avr/cores/arduino/HardwareSerial2.cpp -# LIBS += lib/ArduinoCore-avr/cores/arduino/HardwareSerial3.cpp -LIBS += lib/ArduinoCore-avr/cores/arduino/IPAddress.cpp -LIBS += lib/ArduinoCore-avr/cores/arduino/Print.cpp -LIBS += lib/ArduinoCore-avr/cores/arduino/Stream.cpp -LIBS += lib/ArduinoCore-avr/cores/arduino/WMath.cpp -LIBS += lib/ArduinoCore-avr/cores/arduino/WString.cpp -LIBS += lib/ArduinoCore-avr/cores/arduino/abi.cpp -LIBS += lib/ArduinoCore-avr/cores/arduino/main.cpp -LIBS += lib/ArduinoCore-avr/cores/arduino/new.cpp -LIBS += lib/ArduinoCore-avr/libraries/HID/src/HID.cpp -LIBS += lib/ArduinoCore-avr/libraries/SPI/src/SPI.cpp -# LIBS += lib/ArduinoCore-avr/libraries/SoftwareSerial/src/SoftwareSerial.cpp -LIBS += lib/ArduinoCore-avr/libraries/Wire/src/Wire.cpp -LIBS += lib/pololu-buzzer-arduino/src/PololuBuzzer.cpp -LIBS += lib/pushbutton-arduino/Pushbutton.cpp -LIBS += lib/zumo-32u4-arduino-library/src/QTRSensors.cpp -LIBS += lib/zumo-32u4-arduino-library/src/Zumo32U4IRPulses.cpp -LIBS += lib/zumo-32u4-arduino-library/src/Zumo32U4Encoders.cpp -LIBS += lib/zumo-32u4-arduino-library/src/Zumo32U4IMU.cpp -LIBS += lib/zumo-32u4-arduino-library/src/Zumo32U4Motors.cpp -LIBS += lib/zumo-32u4-arduino-library/src/Zumo32U4ProximitySensors.cpp -LIBS += lib/usb-pause-arduino/USBPause.cpp -LIBS += lib/pololu-hd44780-arduino/PololuHD44780.cpp -LIBS += lib/fastgpio-arduino/FastGPIO.cpp -LIBS += lib/pololu-oled-arduino/src/font.cpp -# LIBS += lib/ArduinoCore-avr/cores/arduino/Tone.cpp -# LIBS += lib/ArduinoCore-avr/libraries/Wire/src/utility/twi.c - -LIBS += lib/ArduinoCore-avr/cores/arduino/WInterrupts.c -LIBS += lib/ArduinoCore-avr/cores/arduino/hooks.c -LIBS += lib/ArduinoCore-avr/cores/arduino/wiring_shift.c -LIBS += lib/ArduinoCore-avr/cores/arduino/wiring_digital.c -LIBS += lib/ArduinoCore-avr/cores/arduino/wiring_analog.c -# LIBS += lib/ArduinoCore-avr/cores/arduino/wiring_pulse.c -LIBS += lib/ArduinoCore-avr/cores/arduino/wiring.c - -SRCS += $(LIBS) - |
