Haptic neural predictor in bilateral tele-operation systems Yuri Boiko Project Presentation for the course ELG 5121 Ottawa University

Slides:

Advertisements

Similar presentations

EYFS Support Meeting Summer 13 Supporting Families and Children.

Advertisements

A New Generation of Surgical Technique: Telesurgery Using Haptic Interfaces By Sarah L. Choy ~ A haptic interface is a force reflecting device which allows.

Coordination of Multi-Agent Systems Mark W. Spong Donald Biggar Willett Professor Department of Electrical and Computer Engineering and The Coordinated.

4. Project Meeting in Ankara 8-9 December 2009 ViLARob Vi rtual L aboratory for A utomation and Rob otics A Transfer of Innovation Project funded by EU.

Bilateral Teleoperation of Multiple Cooperative Robots over Delayed Communication Network: Application Dongjun Lee Mark W. Spong Oscar Martinez-Palafox.

By Bernadette Wagner RN, CNOR Surgical Robotics.  Describe the use of Robots in performing surgery  Describe Hardware and Software programs  Assess.

Mechatronics 1 Week 2. Learning Outcomes By the end of this session, students will understand constituents of robotics, robot anatomy and what contributes.

Communication & Robotics Laboratory 1 Students: Jason Gorski, Aleksandra Krsteva, Yuanyuan Chen Faculty: Imad H. Elhajj Department of Computer Science.

University of Athens, Greece Pervasive Computing Research Group An Online Adaptive Model for Location Prediction University of Athens, Department of Informatics.

Coordinating Central Pattern Generators for Locomotion For repetitive movements such as seen in locomotion, oscillators have been proposed to control the.

Distance Learning: Linking Community College Engineering Students with Engineering Schools (A Proposal) Jack Baskin School of Engineering University of.

JOINT FIRES AND EFFECTS TRAINER SYSTEM (JFETS). We currently rely on service component schools to inform on service capabilities, and train component.

Da Vinci Surgical Robot: Global Deployment and Known Problems Andrew Priddis April 24, 2013.

H exam 1 H CH6: flight in locusts H locust flight H flight system H sensory integration during flight H summary PART 3: MOTOR STRATEGIES #13: FLIGHT IN.

Joint International Master Project Dennis Böck & Dirk C. Aumueller 1.

Open Day Presentation Dr Richard Mitchell Former Head of Department Department of Cybernetics, The University of Reading, UK Cybernetics.

Leslie Luyt Supervisor: Dr. Karen Bradshaw 2 November 2009.

Appendix B: An Example of Back-propagation algorithm

CONTENTS:  Introduction  What is neural network?  Models of neural networks  Applications  Phases in the neural network  Perceptron  Model of fire.

An informal description of artificial neural networks John MacCormick.

The Perceptron. Perceptron Pattern Classification One of the purposes that neural networks are used for is pattern classification. Once the neural network.

Development of Surface EMG Sensor Network and its Application System GRRC Int. Workshop 2008 Youngjin Choi Hanyang University.

Projective Virtual Reality Freund and Rossmann Summarized by Geb Thomas.

A.H. Gosline ( andrewg [at] cim.mcgill.ca) S.E. Salcudean (tims [at] ece.ubc.ca) J. Yan (josephy [at] ece.ubc.ca) Haptic Simulation of Linear Elastic Media.

1 Haptic Systems Mohsen Mahvash Lecture 14 27/1/06.

CI Controllers for Lego Robots - Comparison Study M. Gavalier, M. Hudec, R. Jakša and P. Sinčák Dep. Of.

Teleoperation In Mixed Initiative Systems. What is teleoperation? Remote operation of robots by humans Can be very difficult for human operator Possible.

Simulators for Objective Differentiation of Force-Based Laparoscopic Skills: Towards a Salient Haptic Skills Trainer Dane E. Smith, Joseph Singapogu, Timothy.

2012 Research Expo PhD Topics Professor Peng Shi.

Review: Neural Network Control of Robot Manipulators; Frank L. Lewis; 1996.

Contents  Teleoperated robotic systems  The effect of the communication delay on teleoperation  Data transfer rate control for teleoperation systems.

Cloud Based Robotics.

An Artificial Neural Network Approach to Surface Waviness Prediction in Surface Finishing Process by Chi Ngo ECE/ME 539 Class Project.

LEGO Math by Linda Hamilton Presentation to Marshall University Math Colloquium Fall MM.

7/6/99 MITE1 Fully Parallel Learning Neural Network Chip for Real-time Control Students: (Dr. Jin Liu), Borte Terlemez Advisor: Dr. Martin Brooke.

38/38pt heading for Intro Embedding ICT at Anna Smith November 2005.

Haris Ali (15) Abdul Ghafoor (01) Kashif Zafar (27)

David Wild Supervisor: James Connan Rhodes University Computer Science Department Eye Tracking Using A Simple Webcamera.

DEFENSE EXAMINATION GEORGIA TECH ECE P. 1 Fully Parallel Learning Neural Network Chip for Real-time Control Jin Liu Advisor: Dr. Martin Brooke Dissertation.

@The Finnish Virtual Polytechnic 1 eLearning Together The Finnish Virtual Polytechnic

@the Finnish Virtual Polytehnic 1 The Finnish Virtual Polytechnic Virtual organization, Finnish Polytechnics’ co-operation The Finnish Virtual.

In The Name of Allah The Most Beneficent The Most Merciful 1.

Surgery is an interface problem Surgeon is on the outside target anatomy is on the inside.

Project Paper Presentation Hanlin Wan March 15, 2011.

The past and future of virtual reality simulation in neurologic surgery Longwei F

Презентацию подготовила Хайруллина Ч.А. Муслюмовская гимназия Подготовка к части С ЕГЭ.

SYNCHRONIZATION OF BILATERAL TELEOPERATION SYSTEMS WITH INPUT SATURATION Department of Mechatronics in GIST Seung-Ju Lee Advisor: pf. Hyo-Sung Ahn.

VEX Cortex Video Trainer using ROBOTC

Date of download: 10/20/2017 Copyright © ASME. All rights reserved.

FUZZY NEURAL NETWORKS TECHNIQUES AND THEIR APPLICATIONS

Photorealistic Image Colourization with Generative Adversarial Nets

The University of Texas at Dallas

A Simple Artificial Neuron

NEURAL NETWORK APPROACHES FOR AUTOMOBILE MPG PREDICTION

Thomas Bader, Alexander Wiedemann, Kathrin Roberts,

ريكاوري (بازگشت به حالت اوليه)

Euratom-Tekes Annual Fusion Seminar 2012 Janne Tuominen

Neural Networks & MPIC.

Neural Networks & MPIC.

CHEMICAL SAFETY TRAINING

قوانين برگزاري مناقصات و آيين نامه مالي و معاملاتي دانشگاه علوم پزشكي و خدمات بهداشتي ،درماني تهران

Data science online training.

ҚАЗАҚСТАН РЕСПУБЛИКАСЫНДА САБАҚТЫ ЗЕРТТЕУ ТӘСІЛІН ІСКЕ АСЫРУ

Robotically assisted microsurgery for endoscopic coronary artery bypass grafting Edward R Stephenson, MD, Sachin Sankholkar, MS, Christopher T Ducko,

SmartAnythingEverywhere Initiative

Information Security Update

EAUC Transport Planning Network Group

Challenges in Haptic Communications Over the Tactile Internet

Artificial Muscle based on Flexinol motor wire

Presentation transcript:

Haptic neural predictor in bilateral tele-operation systems Yuri Boiko Project Presentation for the course ELG 5121 Ottawa University

Essay 1, ELG 5121 (26 September 2010) 2 - Principles of bilateral tele-operation - Smyth’s predictor for time delay compensation -Targeted scenarios: - moving elastic objects (example: beating heart surgery) - Haptic description of elastic moving object - Suggested design of haptic neural predictor - Model verification and testing - Conclusion Structure of presentation

Essay 1, ELG 5121 (26 September 2010) 3 Bilateral tele-operation set up

Essay 1, ELG 5121 (26 September 2010) 4 Schematic of bilateral tele-operation

Essay 1, ELG 5121 (26 September 2010) 5 Neural network based Smyth’s predictor

Essay 1, ELG 5121 (26 September 2010) 6 On-line neural network update

Essay 1, ELG 5121 (26 September 2010) 7 Model of moving elastic object X G Force sensor Elastic object Platform The elastic object on the moving platform for haptic experiment

Essay 1, ELG 5121 (26 September 2010) 8 Model: cutting the elastic object X Needle with force sensor Elastic object Platform Needle insertion into the elastic object in haptic experiment

Essay 1, ELG 5121 (26 September 2010) 9 The Chamberlain Group: Beating Heart Trainer Example of the periodically moving elastic object for haptic experiment

Essay 1, ELG 5121 (26 September 2010) 10 Remote robot training with neural network model NN 0 F X G x t1 g t1 x t1 x t2 x t3 Haptic force Input coordinates of force sensor Input coordinates of moving platform g t1 g t2 g t3 Neural network under training

Essay 1, ELG 5121 (26 September 2010) 11 NN 0 NN 1 X G X Trained neural model in the haptic Smith’s predictor x t1 g t1 x t1 x t2 x t3

Essay 1, ELG 5121 (26 September 2010) 12 NN 1 G g t1 Neural haptic predictor for elastic object position

Essay 1, ELG 5121 (26 September 2010) 13 Conclusions - new design of the Smyth predictor is suggested for bilateral tele-operation over moving elastic objects (such as beating heart in surgery); - neural predictor in this new design aims at synchronization of the master and remote robot movements with the movements of the elastic object (such as heart contractions); - extension of the model to the case of cutting the elastic object surface is considered.