AutoRef implementation

The implementation for the AutoRef autonomous referee for RoboCup Middle Size League (MSL) robot soccer is the proposed design of the AutoRef system.

In 2021 contributions by MSD 2020 focused on detecting ball-to-player distance violations.

Introduction

Objective statement

The main objective of the implementation part of the project was to detect violations of the rules related to the distance between the ball and players during the following game states:

• Free kick
• Kick-off
• Corner kick
• Goal kick
• Throw-in
• Dropped-ball
• Penalty kick

These rules are described in Laws number 8, 10, 13, 14, and 15 of the MSL Rulebook.

Motivation

This objective was chosen due to several reasons:

- Past projects analysis has shown that this functionality has never been designed before

- Stakeholder interviews (the MSL referees) have led to the conclusion that this kind of rules are hard to control for a human being

- Proof of concept for the developed functional specification was desirable

- Learning goals of the team members correspond to the technical solutions necessary for the functionality development

Scope of work

The following topics were included in the implementation scope:

• Requirements formulation

• Architectural decomposition development

• Individual code blocks development

• Individual code integration

• Software testing on images and videos

These topics are explained in detail in the following sections.

Process model

Introduction

The development activities of the design team need support from process models. In this project, the V-model is chosen to guide the development procedure from requirement engineering to system validation. Due to the particularity of the project itself, some details of the model have been changed. At the same time, the agile approach was used during system development and combined with the traditional V-model, which makes the project progress more flexible and efficient.

Use of V-model

V-model has the following advantages for the development of the project:

- Design team's project is based on machine vision and software algorithms. V-model was first proposed in the software development environment and has matured in the software development field.

- The project team has five members, all of whom can participate in the development of subsystems and they can be developed at the same time. On the premise that the system architecture is determined, V-model can greatly improve the development efficiency.

- The system development starts from the fourth week, which means that the team needs to complete the system development in five weeks, and the mature and ready-made V-model process can save a lot of time spent on project management.

Based on the general V-model, a detailed test plan has been made for the verification of the system both from functional and performance perspectives.

Based on the requirements derivation results, functional and performance requirements were set up and related tests were planned as shown in the figure above. In this plan, the details of the V-model are supplemented, and more detailed test steps and iterations are added in the test and verification phases. The technical blocks were integrated into the first phase, then several images regarding typical use cases are created from the simulation environment (refer to Section 3) in order to verify the functional requirements. Videos were created to test the performance of the system based on particular scenarios (refer to Section 4). Code was updated iteratively after several times of tests. After the code was verified, a simulated game video was created in the simulation environment to illustrate how the system works in the 'real' world.

Agile approach

Due to the limited project time and various uncertainties in the system development process, Agile approach was applied in the system development process, which is mainly reflected in the system architecture and design choice part.

There are two main difficulties in the development of this project:

- How to implement an efficient and fast detection algorithm?

- How to achieve an accurate image capture in reality?

Usually, the algorithm needs to be executed after the system obtains the image, but it is worth noting that the design of vision system has the following two considerations, which greatly increases the complexity of the system design:

- Fixed camera OR moved camera

- One camera OR multiple cameras

After careful evaluation, we thought that time spent on algorithm development will be greatly reduced in the design of the vision system, which is not the result we want. Therefore, we finally determined the system development scheme based on Agile. That is, a single fixed camera is initially used, and a simulated game situation has been created under the software simulation environment as a reference sample for algorithm development. Optimization of vision system can be carried out after the algorithm is developed.

The main idea is quickly designing and checking the performance of the algorithm we developed, which also confirms the rationality of ‘decision fast’ in the Agile approach.