Robot Motion Simulator

Simulace robota je v současné době nezbytná část v robotickém vývoji a výběr simulátoru hraje důležitou roli. Cílem práce bylo zdůraznit proč je simulace důležitá a zobrazit její výhody a nevýhody. Další cíl bylo zmapovat přehled současných simulačních nástrojů používaných v robotice. Cílem práce bylo navrhnout několik experimentů, které porovnávají prostředí Gazebo a Microsoft Robotics Developer Studio se zaměřením na porovnání fyzikálních vlastností simulátoru. Nakonec bylo zahrnuto vyhodnocení daných experimentů a praktické zkušenosti s danými dvěma nástroji.Robot simulation is currently an essential part of robotic development and selection of the simulator plays an important role. The aim was to highlight why it is important simulation and show its advantages and disadvantages. Another objective was to map overview of current simulation tools used in robotics. The aim was to propose a number of experiments that compare the environmental Gazebo and Microsoft Robotics Developer Studio with a focus on comparing the physical properties of the simulator. Finally, it was included in the evaluation of the experiments and practical experience with the two instruments.

INTEGRATION OF THE SIMULATION ENVIRONMENT FOR AUTONOMOUS ROBOTS WITH ROBOTICS MIDDLEWARE

Robotic simulators have long been used to test code and designs before any actual hardware is tested to ensure safety and efficiency. Many current robotics simulators are either closed source (calling into question the fidelity of their simulations) or are very complicated to install and use. There is a need for software that provides good quality simulation as well as being easy to use. Another issue arises when moving code from the simulator to actual hardware. In many cases, the code must be changed drastically to accommodate the final hardware on the robot, which can possibly invalidate aspects of the simulation. This defense describes methods and techniques for developing high fidelity graphical and physical simulation of autonomous robotic vehicles that is simple to use as well as having minimal distinction between simulated hardware, and actual hardware. These techniques and methods were proven by the development of the Simulation Environment for Autonomous Robots (SEAR) described here. SEAR is a 3-dimensional open source robotics simulator written by Adam Harris in Java that provides high fidelity graphical and physical simulations of user-designed vehicles running user-defined code in user-designed virtual terrain. Multiple simulated sensors are available and include a GPS, triple axis accelerometer, triple axis gyroscope, a compass with declination calculation, LIDAR, and a class of distance sensors that includes RADAR, SONAR, Ultrasonic and infrared. Several of these sensors have been validated against real-world sensors and other simulation software

Systematic literature review of realistic simulators applied in educational robotics context

This paper presents a systematic literature review (SLR) about realistic simulators that can be applied in an educational robotics context. These simulators must include the simulation of actuators and sensors, the ability to simulate robots and their environment. During this systematic review of the literature, 559 articles were extracted from six different databases using the Population, Intervention, Comparison, Outcomes, Context (PICOC) method. After the selection process, 50 selected articles were included in this review. Several simulators were found and their features were also analyzed. As a result of this process, four realistic simulators were applied in the review’s referred context for two main reasons. The first reason is that these simulators have high fidelity in the robots’ visual modeling due to the 3D rendering engines and the second reason is because they apply physics engines, allowing the robot’s interaction with the environment.info:eu-repo/semantics/publishedVersio

Development of distributed control architecture for multi-robot systems

The execution of complex tasks by teams of robots has been widely investigated in the last decades, since many operations are too risky or difficult to be performed by humans or by a single robot. The complexity and variety of applications of mobile robotics make the coordination of teams a big problem, as several topologies of control systems, from simple single processes to large networks with distributed elements that are capable of switching function, may be necessary. Although simple solutions exist, more efficient approaches use distributed communication architectures and components abstraction layers. Available proposals provide many components and interfaces, complicating their understanding and operation. This paper presents a generic control architecture that provides the developer with a small amount of elements implemented safely and on high-performance libraries. The simplicity and modularity of the proposal allow implementation of features such as control of heterogeneous robots, data source and command destination transparency and platform and language independence. The ability to support with reliability, transparency and ease the development of various scenarios of autonomous mobile robotics make the proposed architecture a powerful and valuable tool in the design and operation of these systems.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Centro de Tecnologia da Informação Renato Arche

Hybrid Control of a Segway Platform Developed in MRDS

A Segway RMP200 has been bought by Victoria University for the purpose of making an autonomous robot. The focus of this project was to create reusable services that use existing navigation algorithms to control the Segway within an indoor environment. A SICK LMS100 laser rangefinder was added to detect obstacles and allow localization of the Segway within a known map. A hybrid navigation algorithm consisting of an A* path planner with a dynamic window is used for motion planning and obstacle avoidance. The control system followed a Service Oriented Architecture implemented in Microsoft Robotics Studio using the C# .NET programming language. Four services were created during the project to interface with the SICK LMS100 scanner, control the Segway RMP200, implement the hybrid navigation algorithm and provide a graphic user interface for the system. Tests show that the Segway is able to navigate and maintain localisation within the operating environment by identifying and associating corner and door landmarks within the environment

Robotics software frameworks for multi-agent robotic systems development

Robotics is an area of research in which the paradigm of Multi-Agent Systems (MAS) can prove to be highly useful. Multi-Agent Systems come in the form of cooperative robots in a team, sensor networks based on mobile robots, and robots in Intelligent Environments, to name but a few. However, the development of Multi-Agent Robotic Systems (MARS) still presents major challenges. Over the past decade, a high number of Robotics Software Frameworks (RSFs) have appeared which propose some solutions to the most recurrent problems in robotics. Some of these frameworks, such as ROS, YARP, OROCOS, ORCA, Open-RTM, and Open-RDK, possess certain characteristics and provide the basic infrastructure necessary for the development of MARS. The contribution of this work is the identification of such characteristics as well as the analysis of these frameworks in comparison with the general-purpose Multi-Agent System Frameworks (MASFs), such as JADE and Mobile-C.Ministerio de Ciencia e Innovación TEC2009-10639-C04-02Junta de Andalucía P06-TIC-2298Junta de Andalucía P08-TIC-0386

Simulating use cases for the UAH autonomous electric car

2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, 27-30 Oct. 2019This paper presents the simulation use cases for the UAH Autonomous Electric Car, related with typical driving scenarios in urban environments, focusing on the use of hierarchical interpreted binary Petri nets in order to implement the decision making framework of an autonomous electric vehicle. First, we describe our proposal of autonomous system architecture, which is based on the open source Robot Operating System (ROS) framework that allows the fusion of multiple sensors and the real-time processing and communication of multiple processes in different embedded processors. Then, the paper focuses on the study of some of the most interesting driving scenarios such as: stop, pedestrian crossing, Adaptive Cruise Control (ACC) and overtaking, illustrating both the executive module that carries out each behaviour based on Petri nets and the trajectory and linear velocity that allows to quantify the accuracy and robustness of the architecture proposal for environment perception, navigation and planning on a university Campus.Ministerio de Economía y CompetitividadComunidad de Madri

Acropolis: A Fast Protoyping Robotic Application

Acropolis is an open source middleware robotic framework for fast software prototyping and reuse of program codes. It is made up of a core software and a collection of several extension modules called plugins. Each plugin encapsulates a specific functionality needed for robotic applications. To design a robot behavior, a circuit Of the involved plugins is built with a graphical user interface. A high degree of decoupling between components and a graph-based representation allow the user to build complex robot behaviors with minimal need for code writing. In addition, the Acropolis core is hardware platform independent. Well-known design patterns and layered software architecture are its key features. Through the description of three applications, we illustrate some of its usability

Enhanced vision-based localization and control for navigation of non-holonomic omnidirectional mobile robots in GPS-denied environments

New Zealand’s economy relies on primary production to a great extent, where use of the technological advances can have a significant impact on the productivity. Robotics and automation can play a key role in increasing productivity in primary sector, leading to a boost in national economy. This thesis investigates novel methodologies for design, control, and navigation of a mobile robotic platform, aimed for field service applications, specifically in agricultural environments such as orchards to automate the agricultural tasks. The design process of this robotic platform as a non-holonomic omnidirectional mobile robot, includes an innovative integrated application of CAD, CAM, CAE, and RP for development and manufacturing of the platform. Robot Operating System (ROS) is employed for the optimum embedded software system design and development to enable control, sensing, and navigation of the platform. 3D modelling and simulation of the robotic system is performed through interfacing ROS and Gazebo simulator, aiming for off-line programming, optimal control system design, and system performance analysis. Gazebo simulator provides 3D simulation of the robotic system, sensors, and control interfaces. It also enables simulation of the world environment, allowing the simulated robot to operate in a modelled environment. The model based controller for kinematic control of the non-holonomic omnidirectional platform is tested and validated through experimental results obtained from the simulated and the physical robot. The challenges of the kinematic model based controller including the mathematical and kinematic singularities are discussed and the solution to enable an optimal kinematic model based controller is presented. The kinematic singularity associated with the non-holonomic omnidirectional robots is solved using a novel fuzzy logic based approach. The proposed approach is successfully validated and tested through the simulation and experimental results. Development of a reliable localization system is aimed to enable navigation of the platform in GPS-denied environments such as orchards. For this aim, stereo visual odometry (SVO) is considered as the core of the non-GPS localization system. Challenges of SVO are introduced and the SVO accumulative drift is considered as the main challenge to overcome. SVO drift is identified in form of rotational and translational drift. Sensor fusion is employed to improve the SVO rotational drift through the integration of IMU and SVO. A novel machine learning approach is proposed to improve the SVO translational drift using Neural-Fuzzy system and RBF neural network. The machine learning system is formulated as a drift estimator for each image frame, then correction is applied at that frame to avoid the accumulation of the drift over time. The experimental results and analyses are presented to validate the effectiveness of the methodology in improving the SVO accuracy. An enhanced SVO is aimed through combination of sensor fusion and machine learning methods to improve the SVO rotational and translational drifts. Furthermore, to achieve a robust non-GPS localization system for the platform, sensor fusion of the wheel odometry and the enhanced SVO is performed to increase the accuracy of the overall system, as well as the robustness of the non-GPS localization system. The experimental results and analyses are conducted to support the methodology