DOCSLIB.ORG

Design Industrial Robotics Applications with MATLAB and Simulink

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Design Industrial Robotics Applications with MATLAB and Simulink Design Industrial Robotics Applications with MATLAB and Simulink Presenter Name Here Trends in Industrial Robotics Cobots Grow fastest in shipment terms with CAGR of 20% from 2017 - 2023 Growing trend toward compact robots Increasing share of units shipped in 2023 will be payload <10kg 40% of 80% of 82% of Articulated Robots SCARA Robots Cobots Source: Interact Analysis 2 Evolution of Industrial Robotics Technologies Full Autonomy AI-enabled robots High Autonomy Conditional Autonomy Partial Autonomy Cobot UR5 1st Cobot at Linatex, 2008 1961 Human Assistant Traditional Industrial Robots - Automation Unimate at GM, 1961 No Automation 1961 2008 2019- 3 Autonomous Industrial Robotics Systems Workflow Operates independently, without explicit instructions from a human Connect/Deploy Code Generation ROS Autonomous Algorithms Sense (Observe) Perceive (Orient) Plan (Decide) Control (Act) Encoder Camera Environment Localization Feedback Controls Understanding Force/Torque Contact Path Planning Decision Logic Sensor Switches Object Gain Scheduling Proximity Sensors Detection/Tracking Obstacle Avoidance Trajectory Control S1 S3 S2 Platform Prototype HW Physical Model Actuation Model Environ Model Production HW 4 What we’ll discuss today 01 Challenges in Industry Robot System Design 02 Model-Based Design for Autonomous System Case Study: Pick-and- 03 Place Manipulation Application 04 Concluding Remarks 5 What we’ll discuss today 01 Challenges in Industry Robot System Design 02 Model-Based Design for Autonomous System Case Study: Pick-and- 03 Place Manipulation Application 04 Concluding Remarks 6 Traditional Software Development Cycle Only 6% Of Design/Development Design Concept a Time is Spent on Simulation* * AspenCore - EETimes, “2019 embedded markets study,” EETimes, Tech. Rep., 2019 b Handwritten Code Embedded System c 7 Common Challenges of Industrial Robotics Systems Development Multidomain End-to-End Expertise workflows Technical Depth Complexity of and System Algorithms Stability 8 Key Takeaways In this talk, you will learn Reference workflow for industrial robot development Multi-domain functional areas of Platform, Sensing, Perception, Planning and Control MATLAB and Simulink capabilities to develop new robot algorithms » Kinematic and dynamic models of robots » Perception algorithm design using deep learning » Gazebo co-simulation for sensor models and environment simulation » Path planning with obstacle avoidance » Supervisory logic and control using Stateflow / RL » C/C++ code / ROS nodes generation 9 What we’ll discuss today 01 Challenges in Industry Robot System Design 02 Model-Based Design for Autonomous System Case Study: Pick-and- 03 Place Manipulation Application 04 Concluding Remarks Key to developing robust autonomous system Complete Model-Based Design Workflow Modeling & Test & Simulation Verification Need: An end-to-end development solution that includes modeling & simulation, code generation and test & verification. Code Generation Simulate First and Simulate Often! 11 Full Model-Based Design Workflow Connect / Deploy Connect /Deploy Autonomous Algorithms for Manipulators Plan & Perceive Decide Sense Control Platform Platform 12 What we’ll discuss today 01 Challenges in Industry Robot System Design 02 Model-Based Design for Autonomous System Case Study: Pick-and- 03 Place Manipulation Application 04 Concluding Remarks 13 Pick-and-Place Manipulators Robot Arm Demo Model-Based Design Deep Learning for detecting an object Path planning with collision avoidance 14 Mechanical Modeling Automatic import from CAD Tools CAD Model Platform Sense Perceive Plan & Connect & DeployConnect Decide Simscape Multibody Control Model 15 Actuators Evaluating motor requirements – actuator sizing Platform Sense Perceive Plan & Connect & DeployConnect Decide Robotics System Toolbox Control Simscape 16 Robotics System Environment Modeling Toolbox Connect to an external robotics simulator ROS Toolbox Robot arm simulation with Gazebo Gazebo: Physics-based simulator with sensors and noise 17 Sensing Computer Vision Point cloud processing for pose estimation Toolbox Platform Sense Colorized point cloud Detect table Intel® RealSense™ Perceive RGB-D camera Plan & Connect & DeployConnect Decide Control Point clouds of objects Remove noise and cluster 18 Computer Vision Sensing Toolbox Common sensors and sensing functionalities for autonomous systems Image Processing Toolbox Platform • Support for Common Sensors Sense • Image analysis • Image enhancement Perceive • Visualizing Point Clouds Plan & • Apps Connect & DeployConnect Decide Control 19 Perception Deep learning for object classification Deep Learning Toolbox Platform Object detector using Deep Learning (YOLO v2) Sense • Perceive • • Plan & Connect & DeployConnect Decide Control 20 Perception Object Classification Platform Sense Perceive Plan & Connect & DeployConnect Decide Control 21 Motion Planning Initial Pose X(t0) Final Pose X(tf) Motion Joint Trajectories Joint Limits Planner Q(t) Obstacles Platform Joint positions Follow & track trajectory Sense Reach waypoints Interact with environment Perceive X(t0) q(t0) Manage gripper actions Plan & Connect & DeployConnect Decide q(t1) q(t3) X(tf) Control q(t2) q(tf) 22 Robotics System Motion Planning Toolbox Model Predictive Path Planning + Trajectory Gen + Trajectory Following Control Toolbox Platform Sense Perceive Plan & Connect & DeployConnect Decide Control 23 Motion Control Decision Logic Stateflow Platform Sense Supervisory Control Perceive Plan & Connect & DeployConnect Decide Control 24 Advanced Control: Reinforcement Learning Grasping an object with image inputs Reinforcement Learning Toolbox AGENT ACTION STATE Policy Policy update Reinforceme nt Learning Algorithm REWARD ENVIRONMENT 25 Hardware Connectivity Code Generation Support ROS Toolbox Platform Application Sense Sense1 Sense2 Simulator Perceive Plan & Connect & DeployConnect Middleware Decide Control Act1 HMI1 HMI2 26 Hardware Connectivity Code Generation Support Stateflow Use publisher/subscriber Platform capabilities in Simulink to connect to ROS topics Sense Jetson Xavier (Ubuntu 18.04) Perceive (Bouncy) Node2 Node1 User Application Ethernet Kinova Plan & System Connect & DeployConnect Node3 Object Decide Kortex API Simulink to ROS2 User Application stand-alone node deployment Allows multi-thread, Control multi-core and pseudo real-time 27 Use the same reference workflow For warehouse pick-and-place (storage shelf) This workflow example highlights the use of Robotics System Toolbox collision-checking algorithms, nonlinear MPC, and Stateflow for MATLAB 28 Use the same reference workflow For warehouse pick-and-place (storage shelf) With obstacle avoidance ON (obstacle shown in blue) With obstacle avoidance OFF (obstacle shown in black for reference) 29 Use the same reference workflow For Delta robot for automated parts sorting Connect Sense Perceive Plan & Control Platform Decide What we’ll discuss today 01 Challenges in Industry Robot System Design 02 Model-Based Design for Autonomous System Case Study: Pick-and- 03 Place Manipulation Application 04 Concluding Remarks 31 Full Model-Based Design Workflow Connect / Deploy Connect /Deploy ROS Toolbox Autonomous Algorithms Sensor Fusion and Tracking Tbx Plan & Perceive Navigation Deep Learning Decide Toolbox Toolbox Robotics System Toolbox Stateflow Computer Vision Reinforcement Toolbox Sense Control Learning Toolbox Model Predictive Control Toolbox Platform Platform MATLAB / Simulink Simscape 32 Resources to get started with 33 Concluding Remarks Challenges Develop Software End-to-end workflow in industrial robot with Model-Based for industry robot application Design applications development development Multi-domain Fast Iterations Platform Expertise Complexity of Sense Algorithms Perceive End-to-End Strong Focus workflows on Simulation Plan & Decide Technical Depth and System Stability Control 34 % Thank you! mathworks.com/robotics 35.
Load more

Recommended publications
  • Modular Open Robots Simulation Engine: MORSE
    Modular Open Robots Simulation Engine: MORSE Gilberto Echeverria and Nicolas Lassabe and Arnaud Degroote and Severin´ Lemaignan Abstract— This paper presents MORSE, a new open–source robotics simulator. MORSE provides several features of interest to robotics projects: it relies on a component-based architecture to simulate sensors, actuators and robots; it is flexible, able to specify simulations at variable levels of abstraction according to the systems being tested; it is capable of representing a large variety of heterogeneous robots and full 3D environments (aerial, ground, maritime); and it is designed to allow simu- lations of multiple robots systems. MORSE uses a “Software- (a) A mobile robot in an indoor scene (b) A helicopter and two mobile in-the-Loop” philosophy, i.e. it gives the possibility to evaluate ground robots outdoors the algorithms embedded in the software architecture of the robot within which they are to be integrated. Still, MORSE Fig. 1: Screenshots of the MORSE simulator. is independent of any robot architecture or communication framework (middleware). MORSE is built on top of Blender, using its powerful features and extending its functionality through Python scripts. sensor or path planning for a particular kinematic system Simulations are executed on Blender’s Game Engine mode, [3], [4], [5]; such simulators are highly specialised (“Unitary which provides a realistic graphical display of the simulated Simulation”). On the other hand, some applications require environments and allows exploiting the reputed Bullet physics a more general simulator that can allow the evaluation of engine. This paper presents the conception principles of the simulator and some use–case illustrations.
    [Show full text]
  • A Camera-Realistic Robotics Simulator for Cinematographic Purposes
    2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) October 25-29, 2020, Las Vegas, NV, USA (Virtual) CinemAirSim: A Camera-Realistic Robotics Simulator for Cinematographic Purposes Pablo Pueyo, Eric Cristofalo, Eduardo Montijano, and Mac Schwager Abstract— Unmanned Aerial Vehicles (UAVs) are becoming increasingly popular in the film and entertainment industries, in part because of their maneuverability and perspectives they en- able. While there exists methods for controlling the position and orientation of the drones for visibility, other artistic elements of the filming process, such as focal blur, remain unexplored in the robotics community. The lack of cinematographic robotics solutions is partly due to the cost associated with the cameras and devices used in the filming industry, but also because state- of-the-art photo-realistic robotics simulators only utilize a full in-focus pinhole camera model which does not incorporate these desired artistic attributes. To overcome this, the main contribution of this work is to endow the well-known drone simulator, AirSim, with a cinematic camera as well as extend its API to control all of its parameters in real time, including various filming lenses and common cinematographic properties. Fig. 1. CinemAirSim allows to simulate a cinematic camera inside AirSim. In this paper, we detail the implementation of our AirSim Users can control lens parameters like the focal length, focus distance or modification, CinemAirSim, present examples that illustrate aperture, in real time. The figure shows a reproduction of a scene from “The the potential of the new tool, and highlight the new research Revenant”. We illustrate the filming drone (top left), the current camera opportunities that the use of cinematic cameras can bring to parameters (top right), the movie frame (bottom left) and simulated frame research in robotics and control.
    [Show full text]
  • ORCA - a Physics-Based Robotics Simulation Environment That Supports Distributed Systems Development
    Journal of Software Engineering for Robotics 5(2), September 2014, 13-24 ISSN: 2035-3928 ORCA - a physics-based robotics simulation environment that supports distributed systems development E. Hourdakis1 G. Chliveros1 P. Trahanias1;2 1 Institute of Computer Science, Foundation for Research and Technology, Greece 2 Department of Computer Science, University of Crete, Greece Abstract—Physics-based simulation is becoming an indispensable tool in robotics research, since it enables the evaluation of algorithms in silico, even when the actual robot is not available. Due to this, it is considered a standard practice, prior to any deployment on hardware platforms, and an important part of the development activities in large-scale projects. To cope with this role, simulators must address additional issues related to large-scale model development, including multi-modal processing, provisions for algorithmic integration, and fast, scalable graphics. In the current paper we present ORCA, a versatile, physics-based robotics simulator that integrates a server and a simulation environment into one package. This type of architecture makes the sharing of resources a seamless task, promotes code re-use, and facilitates the integration of individual software modules. To demonstrate the software’s applicability, we discuss three different use-cases, in which ORCA was used at the heart of their development efforts. Index Terms—Physics simulation, robotics, sensor modeling, graphics, distributed processing. 1 INTRODUCTION starting to claim an increased share of the development activ- ities in robotics, which can involve large-scale and integrated UE to the upsurge in processing power, physics engines systems. As such, they must consider issues related to the and CAD modeling software, robotic simulators are D distributed nature of software workflow, for example the ability increasingly being used as a cost-free alternative to actual to facilitate algorithmic integration or process several complex robotic platforms [1], [2].
    [Show full text]
  • Design of a Low-Cost Unmanned Surface Vehicle for Swarm Robotics Research in Laboratory Environments
    DESIGN OF A LOW-COST UNMANNED SURFACE VEHICLE FOR SWARM ROBOTICS RESEARCH IN LABORATORY ENVIRONMENTS by c Calvin Gregory A thesis submitted to the School of Graduate Studies in partial fulfilment of the requirements for the degree of Master of Engineering Faculty of Engineering and Applied Science Memorial University of Newfoundland October 2020 St. John's Newfoundland Abstract Swarm robotics is the study of groups of simple, typically inexpensive agents working collaboratively toward a common goal. Such systems offer several benefits over single- robot solutions: they are flexible, scalable, and robust to the failure of individual agents. The majority of existing work in this field has focused on robots operating in terrestrial environments but the benefits of swarm systems extend to applications in the marine domain as well. The current scarcity of marine robotics platforms suitable for swarm research is detrimental to progress in this field. Of the few that exist, no publicly available unmanned surface vehicles can operate in a laboratory environment; an indoor tank of water where the vessels, temperature, lighting, etc. can be observed and controlled at all times. Laboratory testing is a common intermediate step in the hardware validation of algorithms. This thesis details the design of the microUSV: a small, inexpensive, laboratory-based platform developed to fill this gap. The microUSV system was validated by performing laboratory testing of two algo- rithms: a waypoint-following controller and orbital retrieval. The waypoint-following controller was a simple PI controller implementation which corrects a vessel's speed and heading to seek predetermined goal positions. The orbital retrieval algorithm is a novel method for a swarm of unmanned surface vehicles to gather floating marine contaminants such as plastics.
    [Show full text]
  • Design and Implementation of an Autonomous Robotics Simulator
    DESIGN AND IMPLEMENTATION OF AN AUTONOMOUS ROBOTICS SIMULATOR by Adam Carlton Harris A thesis submitted to the faculty of The University of North Carolina at Charlotte in partial fulfillment of the requirements for the degree of Master of Science in Electrical Engineering Charlotte 2011 Approved by: _______________________________ Dr. James M. Conrad _______________________________ Dr. Ronald R. Sass _______________________________ Dr. Bharat Joshi ii © 2011 Adam Carlton Harris ALL RIGHTS RESERVED iii ABSTRACT ADAM CARLTON HARRIS. Design and implementation of an autonomous robotics simulator. (Under the direction of DR. JAMES M. CONRAD) Robotics simulators are important tools that can save both time and money for developers. Being able to accurately and easily simulate robotic vehicles is invaluable. In the past two decades, corporations, robotics labs, and software development groups have released many robotics simulators to developers. Commercial simulators have proven to be very accurate and many are easy to use, however they are closed source and generally expensive. Open source simulators have recently had an explosion of popularity, but most are not easy to use. This thesis describes the design criteria and implementation of an easy to use open source robotics simulator. SEAR (Simulation Environment for Autonomous Robots) is designed to be an open source cross-platform 3D (3 dimensional) robotics simulator written in Java using jMonkeyEngine3 and the Bullet Physics engine. Users can import custom-designed 3D models of robotic vehicles and terrains to be used in testing their own robotics control code. Several sensor types (GPS, triple-axis accelerometer, triple-axis gyroscope, and a compass) have been simulated and early work on infrared and ultrasonic distance sensors as well as LIDAR simulators has been undertaken.
    [Show full text]
  • Mixed Reality Technologies for Novel Forms of Human-Robot Interaction
    Dissertation Mixed Reality Technologies for Novel Forms of Human-Robot Interaction Dissertation with the aim of achieving a doctoral degree at the Faculty of Mathematics, Informatics and Natural Sciences Dipl.-Inf. Dennis Krupke Human-Computer Interaction and Technical Aspects of Multimodal Systems Department of Informatics Universität Hamburg November 2019 Review Erstgutachter: Prof. Dr. Frank Steinicke Zweitgutachter: Prof. Dr. Jianwei Zhang Drittgutachter: Prof. Dr. Eva Bittner Vorsitzende der Prüfungskomission: Prof. Dr. Simone Frintrop Datum der Disputation: 17.08.2020 “ My dear Miss Glory, Robots are not people. They are mechanically more perfect than we are, they have an astounding intellectual capacity, but they have no soul.” Karel Capek Abstract Nowadays, robot technology surrounds us and future developments will further increase the frequency of our everyday contacts with robots in our daily life. To enable this, the current forms of human-robot interaction need to evolve. The concept of digital twins seems promising for establishing novel forms of cooperation and communication with robots and for modeling system states. Machine learning is now ready to be applied to a multitude of domains. It has the potential to enhance artificial systems with capabilities, which so far are found in natural intelligent creatures, only. Mixed reality experienced a substantial technological evolution in recent years and future developments of mixed reality devices seem to be promising, as well. Wireless networks will improve significantly in the next years and thus, latency and bandwidth limitations will be no crucial issue anymore. Based on the ongoing technological progress, novel interaction and communication forms with robots become available and their application to real-world scenarios becomes feasible.
    [Show full text]
  • Revamping Robotics Education Via University, Community College and In- Dustry Partnership - Year 1 Project Progress
    Paper ID #14439 Revamping Robotics Education via University, Community College and In- dustry Partnership - Year 1 Project Progress Prof. Aleksandr Sergeyev, Michigan Technological University Aleksandr Sergeyev is currently an Associate Professor in the Electrical Engineering Technology program in the School of Technology at Michigan Technological University. Dr. Aleksandr Sergeyev earned his bachelor degree in Electrical Engineering at Moscow University of Electronics and Automation in 1995. He obtained the Master degree in Physics from Michigan Technological University in 2004 and the PhD degree in Electrical Engineering from Michigan Technological University in 2007. Dr. Aleksandr Sergeyev’s research interests include high energy laser propagation through the turbulent atmosphere, developing advanced control algorithms for wavefront sensing and mitigating effects of the turbulent atmosphere, digital inline holography, digital signal processing, and laser spectroscopy. Dr. Sergeyev is a member of ASEE, IEEE, SPIE and is actively involved in promoting engineering education. Dr. Nasser Alaraje, Michigan Technological University Dr. Alaraje is an Associate Professor and Program Chair of Electrical Engineering Technology in the School of Technology at Michigan Tech. Prior to his faculty appointment, he was employed by Lucent Technologies as a hardware design engineer, from 1997- 2002, and by vLogix as chief hardware design engineer, from 2002-2004. Dr. Alaraje’s research interests focus on processor architecture, System-on- Chip design
    [Show full text]
  • Robosim: an Intelligent Simulator for Robotic Systems
    ROBOSIM: AN INTELLIGENT SIMULATOR FOR ROBOTIC SYSTEMS NASA/MSFC, AT01 MSFC, AL 35812 , J George E. Cook, Ph.D. /J 7 Csaba Biegi, Ph.D. James F. Springfield Vanderbilt University School of Engineering Box 1826, Station B Nashville, TN 27235 ABSTRACT The purpose of this paper is to present an update of an intelligent robotics simulator package, ROBOSIM, first introduced at Technology 2000 in 1990. ROBOSIM is used for three-dimensional geometrical modeling of robot manipulators and various objects in their workspace, and for the simulation of action sequences performed by the manipulators. Geometric modeling of robot manipulators has an expanding area of interest because it can aid the design and usage of robots in a number of ways, including: design and testing of manipulators, robot action planning, on-line control of robot manipulators, telerobotic user interface, and training and education. NASA developed ROBOSIM between 1985-88 to facilitate the development of robotics, and used the package to develop robotics for welding, coating, and space operations. ROBOSIM has been further developed for academic use by its co-developer Vanderbilt University, and has been used in both classroom and laboratory environments for teaching complex robotic concepts. Plans are being formulated to make ROBOSIM available to all U.S. engineering/engineering technology schools (over three hundred total with an estimated 10,000+ users per year). INTRODUCTION In the development of advanced robotic systems concepts for use in space and in manufacturing processes, researchers at NASA and elsewhere'have traditionally verified their designs by means of expensive engineering prototype systems. This method, though effective, frequently required numerous re-design/fabrication cycles and a resulting high development cost.
    [Show full text]
  • Social Robotics Agenda.Pdf
    Thank you! The following agenda for social robotics was developed in a project led by KTH and funded by Vinnova. It is a result of cooperation between the following 35 partners: Industry: ABB, Artificial Solutions, Ericsson, Furhat robotics, Intelligent Machines, Liquid Media, TeliaSonera Academia: Göteborgs universitet, Högskolan i Skövde, Karolinska Institutet, KTH, Linköpings universitet, Lunds tekniska högskola, Lunds Universitet, Röda korsets högskola, Stockholms Universtitet, Uppsala Universitet, Örebro universitet Public sector: Institutet för Framtidsstudier, Myndigheten för Delaktighet, Myndigheten för Tillgängliga Medier, Statens medicinsk- etiska råd, Robotdalen, SLL Innovation, Språkrådet End-user organistions: Brostaden, Epicenter, EF Education First, Fryshuset Gymnasium, Hamnskolan, Investor, Kunskapsskolan, Silver Life, Svenskt demenscentrum, Tekniska Museet We would like to thank all partners for their great commitment at the workshops where they shared good ideas and insightful experiences, as well as valuable and important observations of what the future might hold. Agenda key persons: Joakim Gustafson (KTH), Peje Emilsson (Silver Life), Jan Gulliksen (KTH), Mikael Hedelind (ABB), Danica Kragic (KTH), Per Ljunggren (Intelligent Machines), Amy Loutfi (Örebro university), Erik Lundqvist (Robotdalen), Stefan Stern (Investor), Karl-Erik Westman (Myndigheten för Delaktighet), Britt Östlund (KTH) Writing group: editor Joakim Gustafson, co-editor Jens Edlund, Jonas Beskow, Mikael Hedelind, Danica Kragic, Per Ljunggren, Amy
    [Show full text]
  • Simulation of 3D Car Painting Robotic Arm
    Sudan University of science and Technology College of Graduate Studies Simulation of 3D Car Painting Robotic Arm A Thesis Submitted to the College of Graduate Studies at Partial Fulfillment of the Requirements for the Degree of M.Sc. in Mechatronics Engineering By: Alaa Mohamed AbdAlmagiedSead Ahmed Supervisor: D.rFathElrahman Ismael Khalifa Ahmed August 2015 ﭧ ﭨ ﭽﭠ ﭡ ﭢ ﭣ ﭤ ﭼ صدق اهلل العظيم i DEDICATION I want to dedicate this work for my family and friends. It is through their support me that I have been able to focus on my work. My parents taught me that value of learning early on, and have always been supportive me of various decisions to continue my education. ii ACKNOWLEDGEMENT The first thanks and appreciation is for our creator Allah who choose me for this path, and never abandoned me from his mercy. Allah guided me and enlightened our souls and minds through the last semester getting me bounded together as one person and allowing me to know a new definition of the coming life to be which is based on mature thinking and organized group work. Also each of gratitude and thanksgiving for Dr. Fath Elrahman Ismael Khalifa Ahmed as my supervisor, for his continuous guidance and suggestions throughout the preparation of this research. Last but not least thanks are also for my family and friends. iii ABSTRACT Robotic is an electromechanical system, help improve production accuracy, and reduces time and increase production. In this project a study of 3D robotic ARM design, simulation concepts and theories for the painting of cars that deals with the design, construction, operation, and application of robots is conducted.
    [Show full text]
  • Swarm-Based Techniques for Adaptive Navigation Primitives Nathan Metzger
    Santa Clara University Scholar Commons Mechanical Engineering Master's Theses Engineering Master's Theses 5-28-2019 Swarm-Based Techniques for Adaptive Navigation Primitives Nathan Metzger Follow this and additional works at: https://scholarcommons.scu.edu/mech_mstr Part of the Mechanical Engineering Commons Recommended Citation Metzger, Nathan, "Swarm-Based Techniques for Adaptive Navigation Primitives" (2019). Mechanical Engineering Master's Theses. 37. https://scholarcommons.scu.edu/mech_mstr/37 This Thesis is brought to you for free and open access by the Engineering Master's Theses at Scholar Commons. It has been accepted for inclusion in Mechanical Engineering Master's Theses by an authorized administrator of Scholar Commons. For more information, please contact [email protected]. Engineering Date: May 28"\th 2019 ENTITLED BE ACCEPTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF /! .^A/P^' Thesis Advisor Thesis Reader Dr. Christopher Kitts Dr. Robert Marks ) ll"^-^^/ ff-"\.^ Chairman of Department Dr. Drazen Fabris Swarm-Based Techniques for Adaptive Navigation Primitives By Nathan Metzger Graduate Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in Mechanical Engineering in the School of Engineering at Santa Clara University, 2019 Santa Clara, CA Swarm-Based Techniques for Adaptive Navigation Primitives Nathan Metzger Department of Mechanical Engineering Santa Clara University Santa Clara, CA 2019 ABSTRACT Adaptive Navigation (AN) has, in the past, been successfully accomplished by using mobile multi-robot systems (MMS) in highly structured formations known as clusters. Such multi-robot adaptive navigation (MAN) allows for real-time reaction to sensor readings and navigation to a goal location not known a priori.
    [Show full text]
  • Systematic Literature Review of Realistic Simulators Applied in Educational Robotics Context
    sensors Systematic Review Systematic Literature Review of Realistic Simulators Applied in Educational Robotics Context Caio Camargo 1, José Gonçalves 1,2,3 , Miguel Á. Conde 4,* , Francisco J. Rodríguez-Sedano 4, Paulo Costa 3,5 and Francisco J. García-Peñalvo 6 1 Instituto Politécnico de Bragança, 5300-253 Bragança, Portugal; [email protected] (C.C.); [email protected] (J.G.) 2 CeDRI—Research Centre in Digitalization and Intelligent Robotics, 5300-253 Bragança, Portugal 3 INESC TEC—Institute for Systems and Computer Engineering, 4200-465 Porto, Portugal; [email protected] 4 Robotics Group, Engineering School, University of León, Campus de Vegazana s/n, 24071 León, Spain; [email protected] 5 Universidade do Porto, 4200-465 Porto, Portugal 6 GRIAL Research Group, Computer Science Department, University of Salamanca, 37008 Salamanca, Spain; [email protected] * Correspondence: [email protected] Abstract: 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 Citation: Camargo, C.; Gonçalves, J.; analyzed. As a result of this process, four realistic simulators were applied in the review’s referred Conde, M.Á.; Rodríguez-Sedano, F.J.; context for two main reasons. The first reason is that these simulators have high fidelity in the robots’ Costa, P.; García-Peñalvo, F.J.
    [Show full text]