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| =Introduction= | | =Introduction= |
| [[File:CF_on_laptop.jpg|right|300px|]] | | [[File:CF_on_laptop.jpg|right|300px|]] |
| *explain intentions
| | This wiki is the documentation of work delivered by the AutoRef Honors students from the High Tech Systems track in the academic year 2019/2020. |
| *since we cannot test hardware anymore we had to adapt and switch to simulation
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| * Problem definition/challenge or leading question!!!
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| | The goal of this project is to develop an autonomous robot capable of refereeing a robot football play. Such a system can be of benefit by eliminating human error and by using factual data from more sources to make better-evaluated decisions. For this project we have decided to use a drone (quadcopter) as a robot. Most drones are fast and they can change altitude easily, this agility enables a drone to quickly move to a position with a good view, this is a large advantage for a referee. Robot soccer is played on fields of different sizes, and a system using a drone is scalable, whereas for instance a camera on a rail next to a field would not be so scalable. Another reason for using a drone is that it is easy to carry around and it is a small object to work on. |
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| | We made the project goal more specific into the following: |
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| | ''''' "Autonomously assist a football referee in a 2 versus 2 robot soccer match using a drone by enforcing three main rules: out of bounds, free kick, and goal."''''' |
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| | A large obstacle we faced during the project was that the university had to close due to the Covid-19 virus outbreak from March 2020 until the end of the project year. The effects on this project are that the team has not been able to test hardware on the university or work together physically. Considering these changes the team has decided to move our system to a simulation environment, and the work on the hardware has not been finalized. |
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| | | =Team= |
| | | This project was made by the following Honors student in the academic year 2019/2020: |
| == Referee ==
| | *Alvaro Gonzalez |
| make decisions and present information based on the vision part e.g. hey the ball is out of bounds, the robots are bumping into each other, GOAL!
| | *Jake Rap |
| | | *Wolff Voss |
| = Simulation =
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| The simulation environment chosen is CoppeliaSim (also known as V-rep), this environment has a nice intuitive API and works great with ROS. In CoppeliaSim each object (i.e. a drone, or a camera) can have its own (child) script which can communicate with ROS via subscription and publication to topics. The overall architecture can be displayed by the ROS command ''rqt_graph'' while the system is running. The output of this command will be displayed in the following figure.
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| == implementation ==
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| <center>[[File:Honors drone rqt graph.png|750 px|system]]</center>
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| The following text will briefly explain what everything in the figure means according to the software architecture. ''sim_ros_interface'' is the node that is created by the simulator itself and serves as a communication path between the simulator scripts and the rest of the system. What can not be seen in this figure are the individual object scripts within the simulator.
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| A script that belongs to a camera object will publish camera footage from the camera mounted on the drone to the topic ''rawDroneCam''. The ''ball_detector'' node which is run outside the simulator in a separate python file will subscribe to ''rawDroneCam'' and will subsequently get the image from the simulator. It will then extract the relative ball position and size (in pixels) out of each frame and stores it in an object (message type). This message will then be published to the ''ballPos'' topic. At the same time, the ''line_detector'' node will also subscribe to the drones camera feed and will find the lines in the image using Hough transforms. The simulator node (actually a script belonging to the flight controller object) will subscribe to this the ball position topic, get the position of the ball relative to the drone and decide what to do with this information i.e. move in an appropriate manner. Both the line and ball detection also provide an image of their findings to the node ''merge_cam''. Within this node, those two findings will be merged into one image file that displays the lines found and the ball. This loop will run at approximately 30Hz.
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| == Control of Drone in Simulator ==
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| The drone used in the simulation is CoppeliaSims built-in model 'Quadcopter.ttm' which can be found under ''robots->mobile''. This drone uses a script that takes care of the stabilization and drifts. The object script takes the drone's absolute pose and the absolute pose of a 'target' and tries to make the drone follow the target using a PID control loop which actuates on the four motors of the drone. In our actual hardware system, we do not want to use an absolute pose system to follow an object so we will not use the absolute pose of the drone in order to path plan in the simulation either. However, this ability of the drone will be used to stabilize the drone since for the real hardware drone we use an optical flow sensor for this.
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| When the drone receives a message from the /ballPos topic (contains information about the position of the ball relative to the drone) the drone will actuate in the following simple way. The y position of the ball relative to the 2d camera determines whether the ball is too far away or too close. In this scenario, the two front or rear motors will spin harder until the ball is in the middle of the screen again this is pitching motion. Whenever the x position of the ball is too high or low two options are considered to get the ball in the middle again. The drone could either roll (increase thrust for the lateral motors) or yaw (increase trust for diagonal motors). A combination of these is made so that the drone will both roll as well as yaw. With proper tuning of the amount of roll and yaw, the drone will behave somewhat like a human referee in the sense that it will try to minimize the distance to fly whilst keeping the camera stable.
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| == Real game ball simulation ==
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| In order to check whether the drone would be able to follow the ball during a real game situation, the ball trajectory in the simulation was set to replicate the ball movement during the first 3 minutes of the final game of Robocup 2019 Sydney. The position of the ball was logged during the game by one of the Tech United turtles and was downsampled before importing it into the simulator. Although the trajectory of the ball in the simulation resembles that of the ball during the real game, certain quick ball movements cannot be seen in the simulation due to the downsampling. Nevertheless, it is a good measure of the functionality of the control of the drone in a real game situation.
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| == Tutorial Simulation ==
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| After following this tutorial the reader should be able to run a basic simulation of a drone following a yellow object (ball) on a soccer field. This tutorial assumes the reader is on a Linux machine, has installed ROS, and is familiar with its basic functionalities. The next figure gives a sense of what is achieved after this tutorial (it can be clicked to show a video).
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| <center>[[File:Test_gif_drone.gif|center|780px|link=https://drive.google.com/file/d/1Xcl-WHoeJfAQmn44iASLafJr1F9Hzdv-/view?usp=sharing]]</center>
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| *Download and extract the downloadable zip file at the bottom of the page.
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| *Download CoppeliaSim (Edu): https://www.coppeliarobotics.com/downloads
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| *Install it and place the installation folder in a directory e.g. /home/CoppeliaSim/ | |
| *Follow the ROS setup for the simulator: https://www.coppeliarobotics.com/helpFiles/en/ros1Tutorial.htm. It does not have to be followed completely as long as ROS is configured for CoppeliaSim on your machine | |
| *Initialize ROS by opening a terminal (ctrl+alt+t) and typing ''roscore''
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| *Open CoppeliaSim by going to its folder ''e.g. cd /home/CoppeliaSim'', open a terminal and type ''./coppeliaSim.sh''
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| *In the sim, ''file->open scene...'' and locate the ''follow_ball_on_path.ttt'' scene file from the extracted zip.
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| *Open the main script (orange paper under 'scene hierarchy'), find the line ''camp = sim.launchExecutable('/PATH/TO/FILE/ball_detector.py')'' and fill in the path to the extracted zip. Do the same for the ''line_detector.py'' and ''merge_cam.py'' files.
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| *All is set, press the play button to start the simulation.
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| *A window should pop up with the raw camera feed as well as with the processed camera feed and the quadcopter should start to follow the ball! | |
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| =Conclusion=
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| conclusion
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| =Future Work=
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| future work
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| which lines is which
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| which rules and how
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| possible solutions
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| suggestions
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| testing whether scalable
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| test what we achieved in real life, already think about what is most likely to go wrong
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| =project reflection=
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| team reflection, important decisions.
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| what could we have done differently
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| had to set harder 'deadlines' and progression goals
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| =References= | | =References= |
| references
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| =Downloads=
| | Rosebrock, A. (2015). Ball Tracking with OpenCV - PyImageSearch. Retrieved 24 May 2020, from https://www.pyimagesearch.com/2015/09/14/ball-tracking-with-opencv/ |
| *Simulation Files: [[File:Honors_drone_v-rep_1105.zip]]
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| *Empty Robot Soccer Field for CoppeliaSim(V-rep): [[File:V-rep_soccerfield.zip]]
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