Design Patterns in Building Software for a Middle Size League Robot Using Robot Operating System

Purpose– The purpose of this paper is to design intelligent robots operating in such dynamic environments like the RoboCup Middle-Size League (MSL). In the RoboCup MSL, two teams of five autonomous robots play on an 18- × 12-m field. Equipped with sensors and on-board computers, each robot should be able to perceive the environment, make decision and control itself to play the soccer game autonomously.Design/methodology/approach– This paper presents the design of our soccer robots, participating in RoboCup MSL. The mechanical platform, electrical architecture and software framework are discussed separately. The mechanical platform is designed modularly, so easy maintainability is achieved; the electronic architecture is built on industrial standards using PC-based control technique, which results in high robustness and reliability during the intensive and fierce MSL games; the software is developed upon the open-source Robot Operating System (ROS); thus, the advantages of ROS such as modularity, portability and expansibility are inherited.Findings– Based on this paper and the open-source hardware and software, the MSL robots can be re-developed easily to participate in the RoboCup MSL. The robots can also be used in other research and education fields, especially for multi-robot systems and distributed artificial intelligence. Furthermore, the main designing ideas proposed in the paper, i.e. using a modular mechanical structure, an industrial electronic system and ROS-based software, provide a common solution for designing general intelligent robots.Originality/value– The methodology of the intelligent robot design for highly competitive and dynamic RoboCup MSL environments is proposed.

Robot Operating system (ROS) is collection of software frameworks for robot software development. Researchers that work with ROS will have an opportunity to use or to be exact to reuse any software framework from another researcher as long as they use a similar robot platform, or similar device that has been include in the ROS. This paper present a design for a low cost rotating Lidar that compatible with ROS. As all we know distance measurement is the basic for building an autonomous capability for robots. Rotating lidar is among one the most popular device used to acquire this kind of information. However most of them are not suitable for low budget project because of their high cost. The design presented here tries to build a low cost rotating lidar based on Lidar Lite device with the compatibility with the ROS. The design incorporate a control board based on a microcontroller.

Mobile robots have ability to move their entire body and doing some tasks automatically. One of them is RoboCup Middle Size League (MSL) soccer robot. Main systems in the MSL robots are a self-localization. Localization system is very important because it can help some important aspects such as ability to navigate and control. The common localization method of mobile robots is odometry because the process is fast, but the weakness is that the error from odometry will increase over time due to gyro drifting and encoder wheels are slipping. Meanwhile, MSL robots generally use omnivision with the particle filter method for localization. Localization error with omnivision does not increase over time but requires heavy computational processing. Therefore, a sensor fusion system was designed to combine odometry and omnivision. Thus, they can cover each other’s disadvantages, and make localization become more accurate. From the experimental results on the soccer field with size $9{\mathrm {m}}\times 6{\mathrm {m}}$, sensor fusion can provide good localization data. The localization error results x = 10.5 ± 7.8 cm, y = 7.6 ± 6.8 cm, and $\theta$ = 1.9 ± 1.2°, with average time response 1.6 ms. This system is expected to give soccer robot more accurate localization in Robocup MSL matches and help robot navigation when avoiding obstacles.

Ball handling and dribbling are basics soccer techniques. Therefore, handling and dribbling mechanism becomes important component for MSL (Middle Size League) robot soccer. The implementation of active handling and dribbling systems aims to enable the robot to perform dribbling mechanism when maneuvering, while in idle position use handling mechanism. Feedforward and feedback applied as control method in active ball handling and dribbling system. Feedforward control method will give a response to the dribbler wheel based on the motion of robot movement against x , y , and θ axes. Whereas, Feedback control method applied fuzzy inference system by reading the displacement of the arm angle from sensor databased on angle reference of dribbler arm to keep the ball in an ideal position. Implementation geometric calculation for feedforward control toward x , y , and θ axes movement of the robot that it has resulted RMSE value for left motor is 5.75 and for right motor is 7.01. Fuzzy inference system method is used as a feedback control to manipulate the speed and direction of wheel’s rotation as a result ball remains in the ideal position. The value of arm angle displacement and the combination of the two-dribbler arms are used as input for fuzzy inference system. The results of this research show that the application of the combined feedforward and feedback control methods in active handling and dribbling systems can provide robot-dribbling capabilities. Consequently, the robot has a good ability in handling and dribbling mechanism.

Autonomous driving vehicles are currently being developed in many countries. Autonomous driving systems are developed using the Robot Operating System (ROS), which is suitable for the development of robotics and used for various systems of autonomous driving vehicles. However, in the automotive industry, these systems have often been designed using MATLAB/Simulink, can simulate and evaluate models created for autonomous driving. These models cannot be used with the systems based on ROS. To use a model created using MATLAB/Simulink in ROS, it is necessary to rewrite the model for ROS and incorporate it into the autonomous driving system, reducing development efficiency. Therefore, we propose an integrated development framework that can simulate and operate an autonomous driving system based on ROS with MATLAB/Simulink. The proposed framework improves the development efficiency because the model created by MATLAB/Simulink can be used in the systems without a separate incorporation step. In this demonstration, we perform a co-simulation with the autonomous driving system using the proposed framework.

In this paper, we proposed a high precision location estimation algorithm using an intelligent coordination scheme in 2.45GHz RTLS and analyzed an average estimation error distance at 2D coordinates searching-area (300m×300m and 250m×250m). An average error distance was reduced as the number of available reader and received sub-blink increased. Proposed location estimation algorithm satisfied the RTLS specification requirements, 3m radius accuracy when the number of available reader is greater than 3 and the received sub-blink number exceeds 2 times in 250m×250m. Also, an average error distance was saturated within 0.5m~2.5m in case of 250m×250m when the number of available reader is greater than 4 regardless of the number of sub-blink. Also we confirmed that the 3m radius accuracy is 78 percent, and the 2.348m radius accuracy is 72 percent in 300m×300m searching-area when the number of available reader and received sub-blink was 8 and 4 times, respectively. In that case, we confirmed that 3m radius accuracy is 99 percent, and the 2.348m radius accuracy is 97 percent in 250m×250m searching-area.

Customizing computer vision applications for embedded systems is a common and widespread problem in the cyber-physical systems community. Such a customization means parametrizing the algorithm by considering the external environment and mapping the Software application to the heterogeneous Hardware resources by satisfying non-functional constraints like performance, power, and energy consumption. This work presents a framework for the design and simulation of embedded vision applications that integrates the OpenVX standard platform with the Robot Operating System (ROS). The paper shows how the framework has been applied to tune the ORB-SLAM application for an NVIDIA Jetson TX2 board by considering different environment contexts and different design constraints.

A dependency bug is a software fault that manifests itself when accessing an unavailable asset. Dependency bugs are pervasive and we all hate them. This paper presents a case study of dependency bugs in the Robot Operating System (ROS), applying mixed methods: a qualitative investigation of 78 dependency bug reports, a quantitative analysis of 1354 ROS bug reports against 19553 reports in the top 30 GitHub projects, and a design of three dependency linters evaluated on 406 ROS packages. The paper presents a definition and a taxonomy of dependency bugs extracted from data. It describes multiple facets of these bugs and estimates that as many as 15% (!) of all reported bugs are dependency bugs. We show that lightweight tools can find dependency bugs efficiently, although it is challenging to decide which tools to build and difficult to build general tools. We present the research problem to the community, and posit that it should be feasible to eradicate it from software development practice.

This paper proposes an adaptive Monte Carlo location (MCL) algorithm in stages to improve the common problems existed in the traditional MCL method, such as the high computational complexity, and the hijacked circumstance for the mobile robot. Firstly, the current positioned state, namely global localization or local localization, is judged. Secondly, different particles are assigned to conduct localization and tracking based on the different status of positioning allocation. In addition, the accuracy of the positioning system is used to detect whether the robot has been hijacked or not. The research of SLAM carried out on the Robot Operating System (ROS) shows that the proposed method is effective in achieving an accurate localization and reducing the computational complexity. It is suitable for indoor mobile robot SLAM.

Since Gamma et al. published their design patterns, patterns are very popular in the area of software engineering. They provide best practice to handle recurring problems during the software development phase. Three years later, security patterns appeared and provided solutions for security problems. Besides the name analogy, design and security patterns should be very similar except for the security factor. In research and industry software engineering with design patterns is widespread. However, some researchers suspect that security-pattern engineering is made difficult by some issues such as terminology or description form. Detecting adoption problems can help to improve security patterns in future. Thus, they can promote the awareness of security especially in the software maintenance phase where many programmers first give attention to security problems. Therefore, we will compare design and security patterns to find indicators for negative impact on security pattern engineering in software development. We address this issue on inspecting the aspects classification, description form, provided code examples, and usage in the software life-cycle. We determine the degree of maturity of software-security patterns by comparing them to the well-explored design patterns. To achieve the objective, we inspect the pattern terminology and conducted a study on the used description forms including provided UML diagrams and code examples. Moreover, a literature review is conducted to compare their state of research w.r.t. the software life-cycle. The maturity degree of security patterns compared to common design patterns differs and we depict further research opportunities on security patterns.

This paper describes a topological navigation system, based on the description of key-places by a reduced number of parameters that represent images associated to specific locations in configuration space, and the application of the developed system to robotic soccer, through the implementation of the developed algorithms to RoboCup Middle-Size League (MSL) robots, under the scope of the SocRob project (Soccer Robots or Society of Robots). A topological map is associated with a graph, where each node corresponds to a key-place. Using this approach, navigation is reduced to a graph path search. Principal Components Analysis was used to represent key-places from pre-acquired images and to recognize them at navigation time. The method revealed a promising performance navigating between key-places and proved to adapt to different graphs. Furthermore, it leads to a robot programming language based on qualitative descriptions of the target locations in configuration space (e.g., Near Blue Goal with the Goal on its Left). Simulation results of the method application are presented, using a realistic simulator.

The article considers the application of artificial intelligence and computer vision technologies to solve the automation of processing and analysis of botanical micro and macro objects (images of fern spores). Also, there is a problem of developing software for a digital twin of an agrobot. The first problem is an interdisciplinary research aimed at solving applied and fundamental problems in botanical objects' biosystematics and studying microevolutionary processes using computer vision technologies, methods of intelligent image analysis, machine learning, and artificial intelligence. The article presents the developed software module FAST (Functional Automated System Tool) for solving the direct problem — performing measurements from images obtained by scanning electron microscopy, virtual herbaria image library, entomological collections, or images taken in a natural environment.
 The second problem is software development for the digital twin of the agrorobot, designed for precise mechanical processing of plants and soil. The proposed solution includes several components: the control unit — NVIDIA Jetson NANO computing module; the actuator — 6-axis robotic arm; the machine vision unit based on an Intel RealSense camera; the chassis unit — tracked tracks and software drivers and components for their control. The digital twin of the robot considers the environmental conditions and the landscape of the operation area.
 The use of ROS (Robot Operating System) allows minimal effort to transfer a digital model to a physical one (prototype and serial robot) without changing the source code. Furthermore, consideration of the environmental conditions during the programming stage provides opportunities for further development and testing of real-life mathematical models for device control.

In this paper, an image-based humanoid robot pose recognition system is designed and implemented to recognize humanoid robot pose and some objects position in the field. Robot Operating System (ROS) is used to establish the software development framework for the image recognition system. The image recognition system includes feature matching, region of interest (ROI) capture, background subtraction, edge detection, and object segmentation. Then the Inverse Perspective Mapping (IPM) is used to determine the location of humanoid robot and some objects. Experimental results show that the proposed robot pose recognition system can simultaneously recognize and track both of obstacle, humanoid robot and its pose.

The robotics community is concerned with the ability to infer and compare the results from researchers in areas such as vision perception and multi-robot cooperative behavior. To accomplish that task, this paper proposes a real-time indoor visual ground truth system capable of providing accuracy with at least more magnitude than the precision of the algorithm to be evaluated. A multi-camera architecture is proposed under the ROS (Robot Operating System) framework to estimate the 3D position of objects and the implementation and results were contextualized to the Robocup Middle Size League scenario.

This paper presents the design and development of a mecanum wheel-based Automated Guided Vehicle (AGV), serving as a test platform for autonomous system research. The study highlights the essential elements of an Automated Guided Vehicle (AGV) and provides a thorough review of the literature. It explores the information, electrical, and mechanical actuator systems critical to the AGV's functioning. The AGV's navigation system is powered by a Jetson Nano minicomputer, with high-level software development implemented using the Robot Operating System (ROS). The software design focuses on modularity because it facilitates easier changes and maintenance, which is essential for research and rapid testing. The DC motors control subsystem controls AGV mobility by adjusting wheel speed and direction. These motors have incremental encoders that indicate wheel rotation. The Jetson Nano is used in the framework to process data from various sensors and monitor the AGV's performance. Experimental validation was carried out in both static and dynamic environments, where the AGV successfully executed autonomous navigation tasks with an average localization error, motion time, and path deviation. These results demonstrate the system's reliability, accuracy, and potential as a flexible test platform for developing and benchmarking autonomous navigation algorithms in research and educational contexts.