Robotic Drone Referee: Difference between revisions
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===Position Planning and Trajectory planning=== | ===Position Planning and Trajectory planning=== | ||
<p> | <p> | ||
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. | |||
</p> | |||
<p> | |||
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. | |||
</p> | |||
<p> | |||
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. | |||
</p> | |||
<p> | |||
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. | |||
</p> | |||
===Motion Control=== | ===Motion Control=== | ||
Revision as of 14:30, 31 March 2016
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
Description here
System Architecture
Brief
System Architecture - Design Choices
Description here
Detailed System Architecture
Description here
Proof of Concept (POC)
Description here
Use Case-Referee Ball Crossing Pitch Border Line
Description here
Proof of Concept Scope
Description here
Defined Interfaces
Description here
Developed Blocks
Description here
Rule Evaluation
Description here
World Model - Field Line Estimator
Description here
Detection Skill
Description here
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
Description here
Tests & Discussion
Description here
Researched Blocks
Description here
Sensor Fusion
Description here
Cascaded Classifier Detection
Description here
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
Description here
Localization
Description here
Discussion
Description here