(Eg Phd, Mphil, Dclinpsychol) at Th
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
On the Use of Simulation in Robotics
PERSPECTIVE On the use of simulation in robotics: Opportunities, challenges, and suggestions for moving forward PERSPECTIVE HeeSun Choia, Cindy Crumpb, Christian Duriezc, Asher Elmquistd, Gregory Hagere, David Hanf,1, Frank Hearlg, Jessica Hodginsh, Abhinandan Jaini, Frederick Levej, Chen Lik, Franziska Meierl, Dan Negrutd,2, Ludovic Righettim,n, Alberto Rodriguezo, Jie Tanp, and Jeff Trinkleq Edited by Nabil Simaan, Vanderbilt University, Nashville, TN, and accepted by Editorial Board Member John A. Rogers September 30, 2020 (received for review November 16, 2019) The last five years marked a surge in interest for and use of smart robots, which operate in dynamic and unstructured environments and might interact with humans. We posit that well-validated computer simulation can provide a virtual proving ground that in many cases is instrumental in understanding safely, faster, at lower costs, and more thoroughly how the robots of the future should be designed and controlled for safe operation and improved performance. Against this backdrop, we discuss how simulation can help in robotics, barriers that currently prevent its broad adoption, and potential steps that can eliminate some of these barriers. The points and recommendations made concern the following simulation-in-robotics aspects: simulation of the dynamics of the robot; simulation of the virtual world; simulation of the sensing of this virtual world; simulation of the interaction between the human and the robot; and, in less depth, simulation of the communication between robots. This Perspectives contribution summarizes the points of view that coalesced during a 2018 National Science Foundation/Department of Defense/National Institute for Standards and Technology workshop dedicated to the topic at hand. -
Numerical Methods for Large Scale Non-Smooth Multibody Problems
Politecnico di Milano 6/11/2014 Numerical Methods for Large Scale Non-Smooth Multibody Problems Ing. Alessandro Tasora Dipartimento di Ingegneria Industriale Università di Parma, Italy [email protected] http://ied.unipr.it/tasora Numerical Methods for Large-Scale Multibody Problems Politecnico di Milano, November 2014 A.Tasora, Dipartimento di Ingegneria Industriale, Università di Parma, Italy slide n. 2 Let us go on and win glory for ourselves, or yield it to others Homer, Iliad ΑΧΙΛΛΕΥΣ 1 Numerical Methods for Large-Scale Multibody Problems Politecnico di Milano, November 2014 A.Tasora, Dipartimento di Ingegneria Industriale, Università di Parma, Italy slide n. 3 Multibody simulation today MultibodyOutlook simulation tomorrow THE COMPLEXITY CHALLENGE Numerical Methods for Large-Scale Multibody Problems Politecnico di Milano, November 2014 A.Tasora, Dipartimento di Ingegneria Industriale, Università di Parma, Italy slide n. 4 Background Joint work in the multibody field with - M.Anitescu (ARGONNE National Labs, Chicago University) - D.Negrut & al. (University of Wisconsin – Madison) - J.Kleinert & al. (Fraunhofer ITWM, Germany) - F.Pulvirenti & al. (Ferrari Auto, Italy) - NVidia Corporation (USA) - A.Jain (NASA – JPL) - S.Negrini & al. (Politecnico di Milano, Italy) - ENSAM Labs (France) - Realsoft OY (Finland) - Cineca supercomputing (Italy) 2 Numerical Methods for Large-Scale Multibody Problems Politecnico di Milano, November 2014 A.Tasora, Dipartimento di Ingegneria Industriale, Università di Parma, Italy slide n. 5 Structure of this lecture Sections • Concepts and applications • Coordinate transformations • Dynamics: a theoretical background • A typical direct solver for classical MB problems • Iterative method for nonsmooth dynamics • Software implementation • C++ implementation of the HyperOCTANT solver in Chrono::Engine • Examples • Future challenges Numerical Methods for Large-Scale Multibody Problems Politecnico di Milano, November 2014 A.Tasora, Dipartimento di Ingegneria Industriale, Università di Parma, Italy slide n. -
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. -
Physics-Based Simula1on
Physics-based Simula1on • simple (independent par1cles), or complex (robust colliding, stacking, sliding 3D rigid bodies) • many many simulators! – PhysX (Unity, Unreal), Bullet, Open Dynamics Engine, MuJoCo, Havok, Box2D, Chipmunk, OpenSim, RBDL, Simulink (MATLAB), ADAMS, SD/FAST, DART, Vortex, SOFA, Avatar, Project Chrono, Cannon.js, … – many course projects, theses, abandon-ware Resources • hUps://processing.org/examples/ see “Simulate”; 2D par1cle systems • Non-convex rigid bodies with stacking 3D collision processing and stacking hUp://www.cs.ubc.ca/~rbridson/docs/rigid_bodies.pdf • Physically-based Modeling, course notes, SIGGRAPH 2001, Baraff & Witkin hUp://www.pixar.com/companyinfo/research/pbm2001/ • Doug James CS 5643 course notes hUp://www.cs.cornell.edu/courses/cs5643/2015sp/ • Rigid Body Dynamics, Chris Hecker hUp://chrishecker.com/Rigid_Body_Dynamics • Video game physics tutorial hUps://www.toptal.com/game/video-game-physics-part-i-an-introduc1on-to-rigid-body-dynamics • Box2D javascript live demos hUp://heikobehrens.net/misc/box2d.js/examples/ • Rigid body collisions javascript demo hUps://www.myphysicslab.com/engine2D/collision-en.html • Rigid Body Collision Reponse, Michael Manzke, course slides hUps://www.scss.tcd.ie/Michael.Manzke/CS7057/cs7057-1516-09-CollisionResponse-mm.pdf • Rigid Body Dynamics Algorithms. Roy Featherstone, 2008 • Par1cle-based Fluid Simula1on for Interac1ve Applica1ons, SCA 2003, PDF • Stable Fluids, Jos Stam, SIGGRAPH 1999. interac1ve demo: hUps://29a.ch/2012/12/16/webgl-fluid-simula1on Simula1on -
Physics-Based Simulabon
Physics-based Simulaon • simple (independent par1cles), or complex (robust colliding, stacking, sliding 3D rigid bodies) • many many simulators! – PhysX (Unity, Unreal), Bullet, Open Dynamics Engine, MuJoCo, Havok, Box2D, Chipmunk, OpenSim, RBDL, Simulink (MATLAB), ADAMS, SD/FAST, DART, Vortex, SOFA, Avatar, Project Chrono, Cannon.js, … – many course projects, theses, abandon-ware Resources • hUps://processing.org/examples/ see “Simulate”; 2D par1cle systems • Non-convex rigid bodies with stacking 3D collision processing and stacking hUp://www.cs.ubc.ca/~rbridson/docs/rigid_bodies.pdf • Physically-based Modeling, course notes, SIGGRAPH 2001, Baraff & Witkin hUp://www.pixar.com/companyinfo/research/pbm2001/ • Doug James CS 5643 course notes hp://www.cs.cornell.edu/courses/cs5643/2015sp/ • Rigid Body Dynamics, Chris Hecker hUp://chrishecker.com/Rigid_Body_Dynamics • Video game physics tutorial hUps://www.toptal.com/game/video-game-physics-part-i-an-introduc1on-to-rigid-body-dynamics • Box2D javascript live demos hUp://heikobehrens.net/misc/box2d.js/examples/ • Rigid body collisions javascript demo hUps://www.myphysicslab.com/engine2D/collision-en.html • Rigid Body Collision Reponse, Michael Manzke, course slides hUps://www.scss.tcd.ie/Michael.Manzke/CS7057/cs7057-1516-09-CollisionResponse-mm.pdf • Rigid Body Dynamics Algorithms. Roy Featherstone, 2008 • Par1cle-based Fluid Simulaon for Interac1ve Applicaons, SCA 2003, PDF • Stable Fluids, Jos Stam, SIGGRAPH 1999. interac1ve demo: hUps://29a.ch/2012/12/16/webgl-fluid-simulaon Simulaon Basics -
Outlook Simulation Tomorrow the COMPLEXITY CHALLENGE
Politecnico di Milano 7/11/2013 Numerical Methods for Large Scale Non-Smooth Multibody Problems Ing. Alessandro Tasora Dipartimento di Ingegneria Industriale Università di Parma, Italy [email protected] http://ied.unipr.it/tasora Numerical Methods for Large-Scale Multibody Problems Politecnico di Milano, January 2013 A.Tasora, Dipartimento di Ingegneria Industriale, Università di Parma, Italy slide n. 2 Multibody simulation today MultibodyOutlook simulation tomorrow THE COMPLEXITY CHALLENGE 1 Numerical Methods for Large-Scale Multibody Problems Politecnico di Milano, January 2013 A.Tasora, Dipartimento di Ingegneria Industriale, Università di Parma, Italy slide n. 3 Background Joint work in the multibody field with - M.Anitescu (ARGONNE National Labs, Chicago University) - D.Negrut & al. (University of Wisconsin – Madison) - J.Kleinert & al. (Fraunhofer ITWM, Germany) - F.Pulvirenti & al. (Ferrari Auto, Italy) - NVidia Corporation (USA) - A.Jain (NASA – JPL) - S.Negrini & al. (Politecnico di Milano, Italy) - ENSAM Labs (France) - Realsoft OY (Finland) Numerical Methods for Large-Scale Multibody Problems Politecnico di Milano, January 2013 A.Tasora, Dipartimento di Ingegneria Industriale, Università di Parma, Italy slide n. 4 Structure of this lecture Sections • Concepts and applications • Coordinate transformations • Dynamics: a theoretical background • A typical direct solver for classical MB problems • Iterative method for nonsmooth dynamics • Software implementation • C++ implementation of the HyperOCTANT solver in Chrono::Engine • Examples • Future challenges 2 Numerical Methods for Large-Scale Multibody Problems Politecnico di Milano, January 2013 A.Tasora, Dipartimento di Ingegneria Industriale, Università di Parma, Italy slide n. 5 Section Multibody simulation: concepts and applications Numerical Methods for Large-Scale Multibody Problems Politecnico di Milano, January 2013 A.Tasora, Dipartimento di Ingegneria Industriale, Università di Parma, Italy slide n. -
A Hybrid Peridynamics and Physics Engine Approach
Available online at www.sciencedirect.com ScienceDirect Comput. Methods Appl. Mech. Engrg. 348 (2019) 334–355 www.elsevier.com/locate/cma Modeling continuous grain crushing in granular media: A hybrid peridynamics and physics engine approach Fan Zhu, Jidong Zhao∗ Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong Special Administrative Region Received 29 August 2018; received in revised form 14 January 2019; accepted 14 January 2019 Available online 8 February 2019 Abstract Numerical modeling of crushable granular materials is a challenging but important topic across many disciplines of science and engineering. Commonly adopted modeling techniques, such as those based on discrete element method, often over-simplify the complex physical processes of particle breakage and remain a far cry from being adequately rigorous and efficient. In this paper we propose a novel, hybrid computational framework combining peridynamics with a physics engine to simulate crushable granular materials under mechanical loadings. Within such framework, the breakage of individual particles is analyzed and simulated by peridynamics, whilst the rigid body motion of particles and inter-particle interactions are modeled by the physics engine based on a non-smooth contact dynamics approach. The hybrid framework enables rigorous modeling of particle breakage and allows reasonable simulation of irregular particle shapes during the continuous breakage process, overcoming a glorious drawback/challenge faced by many existing methods. We further demonstrate the predictive capability of the proposed method by a simulation of one-dimensional compression on crushable sand, where Weibull statistical distribution on the particle strength is implemented. The simulation results exhibit reasonable agreement with experimental observations with respect to normal compression line, particle size distribution, fractal dimension, as well as particle morphology. -
Gpu-Accelerated Applications Gpu‑Accelerated Applications
GPU-ACCELERATED APPLICATIONS GPU-ACCELERATED APPLICATIONS Accelerated computing has revolutionized a broad range of industries with over six hundred applications optimized for GPUs to help you accelerate your work. CONTENTS 1 Computational Finance 62 Research: Higher Education and Supercomputing NUMERICAL ANALYTICS 2 Climate, Weather and Ocean Modeling PHYSICS 2 Data Science and Analytics SCIENTIFIC VISUALIZATION 5 Artificial Intelligence 68 Safety and Security DEEP LEARNING AND MACHINE LEARNING 71 Tools and Management 13 Federal, Defense and Intelligence 79 Agriculture 14 Design for Manufacturing/Construction: 79 Business Process Optimization CAD/CAE/CAM CFD (MFG) CFD (RESEARCH DEVELOPMENTS) COMPUTATIONAL STRUCTURAL MECHANICS DESIGN AND VISUALIZATION ELECTRONIC DESIGN AUTOMATION INDUSTRIAL INSPECTION Test Drive the 29 Media and Entertainment ANIMATION, MODELING AND RENDERING World’s Fastest COLOR CORRECTION AND GRAIN MANAGEMENT COMPOSITING, FINISHING AND EFFECTS Accelerator – Free! (VIDEO) EDITING Take the GPU Test Drive, a free and (IMAGE & PHOTO) EDITING easy way to experience accelerated ENCODING AND DIGITAL DISTRIBUTION computing on GPUs. You can run ON-AIR GRAPHICS your own application or try one of ON-SET, REVIEW AND STEREO TOOLS the preloaded ones, all running on a WEATHER GRAPHICS remote cluster. Try it today. www.nvidia.com/gputestdrive 44 Medical Imaging 47 Oil and Gas 48 Life Sciences BIOINFORMATICS MICROSCOPY MOLECULAR DYNAMICS QUANTUM CHEMISTRY (MOLECULAR) VISUALIZATION AND DOCKING Computational Finance APPLICATION NAME COMPANYNAME PRODUCT DESCRIPTION SUPPORTED FEATURES GPU SCALING Accelerated Elsen Secure, accessible, and accelerated • Web-like API with Native bindings for Multi-GPU Computing Engine back-testing, scenario analysis, Python, R, Scala, C Single Node risk analytics and real-time trading • Custom models and data streams designed for easy integration and rapid development. -
LAPPEENRANTA UNIVERSITY of TECHNOLOGY LUT School of Energy Systems LUT Mechanical Engineering
LAPPEENRANTA UNIVERSITY OF TECHNOLOGY LUT School of Energy Systems LUT Mechanical Engineering Niko Niemi COMPARISON OF OPEN DYNAMICS ENGINE, CHRONO AND MEVEA IN SIMPLE MULTIBODY APPLICATIONS Examiners: Professor Aki Mikkola D. Sc. (Tech.) Grzegorz Orzechowski ABSTRACT Lappeenranta University of Technology LUT School of Energy Systems LUT Mechanical Engineering Niko Niemi Comparison of Open Dynamics Engine, Chrono and MeVEA in simple multibody applications Master’s thesis 2017 80 pages, 24 figures, 11 tables and 1 appendix Examiners: Professor Aki Mikkola D. Sc. (Tech.) Grzegorz Orzechowski Keywords: multibody dynamics, physics engine, Open Dynamics Engine, MeVEA, Chrono In this thesis, simulation speed and accuracy of Open Dynamics Engine (ODE), Chrono and MeVEA are compared. To this end, a simple multibody model is created for benchmarking purposes in each of the software. The feasibility of ODE in biomechanical modeling is also studied by creating two hands in the software environment. The theory behind multibody dynamics and the physics engines is reviewed to gain a better understanding of the issues affecting performance. Emphasis is on Chrono and ODE as they are open-source software. Each software is benchmarked using the same multibody system, which is a simple slider- crank mechanism consisting of three bodies. A torque is applied to the crank, which moves the slider back and forth and this is simulated for a set amount of simulation steps corresponding to two seconds in real-time. In the speed comparison, the simulation is run to test how long it takes to solve it. In the accuracy benchmark, the speed of the slider at each step is compared to a widely used multibody simulation software, Adams. -
Gpu-Accelerated Applications Gpu‑Accelerated Applications
GPU-ACCELERATED APPLICATIONS GPU-ACCELERATED APPLICATIONS Accelerated computing has revolutionized a broad range of industries with over six hundred applications optimized for GPUs to help you accelerate your work. CONTENTS 1 Computational Finance 62 Research: Higher Education and Supercomputing NUMERICAL ANALYTICS 2 Climate, Weather and Ocean Modeling PHYSICS 2 Data Science and Analytics SCIENTIFIC VISUALIZATION 5 Artificial Intelligence 68 Safety and Security DEEP LEARNING AND MACHINE LEARNING 71 Tools and Management 13 Public Sector 79 Agriculture 14 Design for Manufacturing/Construction: 79 Business Process Optimization CAD/CAE/CAM CFD (MFG) CFD (RESEARCH DEVELOPMENTS) COMPUTATIONAL STRUCTURAL MECHANICS DESIGN AND VISUALIZATION ELECTRONIC DESIGN AUTOMATION INDUSTRIAL INSPECTION Test Drive the 29 Media and Entertainment ANIMATION, MODELING AND RENDERING World’s Fastest COLOR CORRECTION AND GRAIN MANAGEMENT COMPOSITING, FINISHING AND EFFECTS Accelerator – Free! (VIDEO) EDITING Take the GPU Test Drive, a free and (IMAGE & PHOTO) EDITING easy way to experience accelerated ENCODING AND DIGITAL DISTRIBUTION computing on GPUs. You can run ON-AIR GRAPHICS your own application or try one of ON-SET, REVIEW AND STEREO TOOLS the preloaded ones, all running on a WEATHER GRAPHICS remote cluster. Try it today. www.nvidia.com/gputestdrive 44 Medical Imaging 47 Oil and Gas 48 Life Sciences BIOINFORMATICS MICROSCOPY MOLECULAR DYNAMICS QUANTUM CHEMISTRY (MOLECULAR) VISUALIZATION AND DOCKING Computational Finance APPLICATION NAME COMPANYNAME PRODUCT DESCRIPTION SUPPORTED FEATURES GPU SCALING Accelerated Elsen Secure, accessible, and accelerated • Web-like API with Native bindings for Multi-GPU Computing Engine back-testing, scenario analysis, Python, R, Scala, C Single Node risk analytics and real-time trading • Custom models and data streams designed for easy integration and rapid development. -
Enabling Artificial Intelligence Studies in Off-Road Mobility Through Physics-Based Simulation of Multi-Agent Scenarios
2020 NDIA GROUND VEHICLE SYSTEMS ENGINEERING AND TECHNOLOGY SYMPOSIUM Autonomy, Artificial Intelligence & Robotics (AAIR) Technical Session August 11-13, 2020 { Novi, Michigan ENABLING ARTIFICIAL INTELLIGENCE STUDIES IN OFF-ROAD MOBILITY THROUGH PHYSICS-BASED SIMULATION OF MULTI-AGENT SCENARIOS D. Negrut, R. Serban, A. Elmquist, J. Taves, A. Young University of Wisconsin { Madison Madison, WI A. Tasora, S. Benatti Dipartimento di Ingegneria ed Architettura University of Parma, Italy ABSTRACT We describe a simulation environment that enables the design and testing of control policies for off- road mobility of autonomous agents. The environment is demonstrated in conjunction with the design and assessment of a reinforcement learning policy that uses sensor fusion and inter-agent communi- cation to enable the movement of mixed convoys of conventional and autonomous vehicles. Policies learned on rigid terrain are shown to transfer to hard (silt-like) and soft (snow-like) deformable ter- rains. The enabling simulation environment, which is Chrono-centric, is used as follows: the training occurs in the GymChrono learning environment using PyChrono, the Python interface to Chrono. The GymChrono-generated policy is subsequently deployed for testing in SynChrono, a scalable, cluster- deployable multi-agent testing infrastructure that uses MPI. The Chrono::Sensor module simulates sensing channels used in the learning and inference processes. The software stack described is open source. Relevant movies: [1]. 1 INTRODUCTION perform more thorough testing, and produce more performant and safer designs. Simulation is not Computer simulation has been extensively used in a silver bullet as it has its limitations, first of all the design and analysis of various automation as- the issue of simulation-to-reality transfer [3], which pects tied to on-road mobility, see, for instance pertains to the failure of control policies derived in [2].