Robotic Drone Referee
Abstract
Refereeing any kind of sport is not an easy job and the decision making procedure involves a lot of variables that cannot be fully taken into account at all times. Human refereeing has a lot of limitations but it has been the only way of proceeding until now. Due to the lack of information, referees sometimes make wrong decisions that can sometimes change the flow of the game or even make it unfair. The purpose of this project is to develop an autonomous drone that will serve as a referee for any kind of soccer match. The robotic referee should be able to make objective decisions taking into account all the possible information available. Thus, information regarding the field, players and ball should be assessed in real-time. This project will deliver an efficient, innovative, extensible and flexible system architecture able to cope with real time requirements and well-known robotic systems constraints.
Introduction - Project Description
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System Architecture
Brief
System Architecture - Design Choices
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Detailed System Architecture
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Proof of Concept (POC)
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Use Case-Referee Ball Crossing Pitch Border Line
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Proof of Concept Scope
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Defined Interfaces
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Developed Blocks
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Rule Evaluation
Rule evaluation encloses all refereeing soccer rules that are currently taken into account in this project. The rule evaluation set consists of the following:
World Model - Field Line Estimator
The detection module needs a prediction module to predict the view that the camera on drone would have in each moment. Because the localization method does not use image processing and there is no need to provide data of camera view other than side lines, the line predictor block just provides data of visible side lines, visible to drone camera.[Continue...]
Detection Skill
The Detection skill is in charge of the vision-based detection within the frame of reference. Currently, it consists of the following developed sub-tasks:
World Model - Ultra Wide Band System (UWBS) - Trilateration
One of the most important building blocks for the drone referee is a method for positioning. At all times, the drone state, namely the set {X,Y,Z,Yaw}, should be known in order to perform the refereeing duties. Of the drone state, Z and the Yaw are measured by either the drone sensor suite or other programs as they are required for the low-level control of the drone. However, in order to localize w.r.t. the field and to find X and Y, a solution has to be found. To this end, several concepts were composed. Of those concepts, trilateration using Ultra Wide Band Anchors (UWB) was realized. For more details, go to Ultra Wide Band System - Trilateration. First, the rejected concepts are shortly listed, followed by a detailed explanation of the UWB system.
Integration
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Tests & Discussion
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Researched Blocks
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Sensor Fusion
In the designed system there are some environment sensing methods that give the same information about the environment. It can be proven that measurement updates can increase accuracy of a probabilistic function. As an example, Localization block can use UltraWide band and acceleration sensors to localize the position of drone. This data fusion is desirable because the UltraWide band system has a high accuracy but low response time, and acceleration sensors have lower accuracy but a higher response time. The other system that can benefit from sensor fusion is the psi angel block. The psi angel is needed for drone motion control and also for other detection blocks. The data of the psi angle are coming from the drone’s magneto meter and gyroscope. The magneto meter gives the psi angle with a rather high error. The gyroscope gives the derivation of the psi angle. The data provided by the gyroscope has a high accuracy but because it is derivation of psi angle the uncertainty increases by time. Sensor fusion can be used in this case to correct data coming from both sensors.
Cascaded Classifier Detection
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Position Planning and Trajectory planning
Creating a reference for the drone is not as simple as just following the ball, especially in the case of multiple drones. While the ball is of interest, many objects in the field are. For instance, if near a line or the goal, it is better to have this in frame. In case of a player it might be better to be prepared for a kick. Furthermore, with multiple drones the references and trajectory paths should never cross and the extra drones should be in positions of use.
As this is a complex problem, it should be best to have a configurable solution. In case a drone gets another assignment, from following the ball to watching the line, the same algorithm should be applicable. At the beginning of the project, a good positioning solution was within the scope, but as the project progressed it was omitted. For this reason a conceptual solution was devised, but no implementation was made. The proposal was to use a weighted algorithm, different factors should influence the position, and the influence should be adjustable using weights. Based on these criteria, a potential field algorithm was selected from a few candidates.
In a potential field algorithm, an artificial potential or gravitational field is overlaid on the actual world map. The agent using the algorithm will go from A to B following this field. So an obstacle is represented a repulsive object while other things can be classified as attracting objects. In this way it is used for path planning. The proposed implementation would be different in the way that it would be used to find the reference position to go to. For instance, the ball and the goal would be an attractor while other drones could be repulsors. By configuring the strength of the different attractors and repulsors, a different drone task could be represented. The implementation requires more study as well as some experimentation. This required time that was not available. However, it could be a possible area of interest to other to continue with this project.
In the scope of this project trajectory planning never received a lot of attention. As the purpose for the demo was always to implement only one drone, the trajectory could just be a straight line. However, in case of other drones, jumping players or bouncing balls, this is not sufficient. If a potential field algorithm is already being researched, it could of course also be implemented for the trajectory planning. However, swarm-based flying and intelligent positioning are fields in which a lot of research is conducted right now. As such, for trajectory planning it might be better to do more extensive research into the state of the art.
Motion Control
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Localization
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Discussion
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