Locomotion Exploiting Body Dynamics - Semester Project - Student: Matteo de Giacomi Supervisor: Jonas Buchli.

Slides:



Advertisements
Similar presentations
Automation and Control of a limited size parking lot using PIC18 Microcontroller Alaa Sharif Ali Ghamlouch Zaher Khattab April 2011 Presented to: Dr. Youmin.
Advertisements

Add and Use a Sensor & Autonomous For FIRST Robotics
Accelerometer-based User Interfaces for the Control of a Physically Simulated Character Takaaki Shiratori Jessica K. Hodgins Carnegie Mellon University.
Delft University of TechnologyDelft Centre for Mechatronics and Microsystems Introduction Factory robots use trajectory control; the desired angles of.
Angular Variables Linear Angular Position m s deg. or rad. q Velocity
Robotics applications of vision-based action selection Master Project Matteo de Giacomi.
Benjamin Stephens Carnegie Mellon University 9 th IEEE-RAS International Conference on Humanoid Robots December 8, 2009 Modeling and Control of Periodic.
1 Predator-Prey Oscillations in Space (again) Sandi Merchant D-dudes meeting November 21, 2005.
Design of Attitude and Path Tracking Controllers for Quad-Rotor Robots using Reinforcement Learning Sérgio Ronaldo Barros dos Santos Cairo Lúcio Nascimento.
‘Initial state’ coordinations reproduce the instant flexibility for human walking By: Esmaeil Davoodi Dr. Fariba Bahrami In the name of GOD May, 2007 Reference:
Control Design to Achieve Dynamic Walking on a Bipedal Robot with Compliance Young-Pil Jeon.
Shape and Dynamics in Human Movement Analysis Ashok Veeraraghavan.
Active Calibration of Cameras: Theory and Implementation Anup Basu Sung Huh CPSC 643 Individual Presentation II March 4 th,
The City College of New York 1 Prepared by Dr. Salah Talha Mobot: Mobile Robot Introduction to ROBOTICS.
Sandra Wieser Alexander Spröwitz Auke Jan Ijspeert.
Mechatronics 1 Week 2. Learning Outcomes By the end of this session, students will understand constituents of robotics, robot anatomy and what contributes.
Feedback of Biological Systems Specifically Humans Ryan Bussis Samuel Stearley.
SSS: A Hybrid Architecture Applied to Robot Navigation Jonathan H. Connell IBM T.J. Watson Research Center Review Paper By Kai Xu What’s this?
1 Design of a controller for sitting of infants Semester Project July 5, 2007 Supervised by: Ludovic Righetti Prof. Auke J. Ijspeert Presented by: Neha.
Fast and Robust Legged Locomotion Sean Bailey Mechanical Engineering Design Division Advisor: Dr. Mark Cutkosky May 12, 2000.
Coordinating Central Pattern Generators for Locomotion For repetitive movements such as seen in locomotion, oscillators have been proposed to control the.
Semester Project Defense: Locomotion in Modular Robots: YaMoR Host 3 and Roombots. Simon Lépine Supervisors: Auke Ijspeert Alexander Spröwitz.
Impact of Different Mobility Models on Connectivity Probability of a Wireless Ad Hoc Network Tatiana K. Madsen, Frank H.P. Fitzek, Ramjee Prasad [tatiana.
Fast Walking and Modeling Kicks Purpose: Team Robotics Spring 2005 By: Forest Marie.
Locomotion in modular robots using the Roombots Modules Semester Project Sandra Wieser, Alexander Spröwitz, Auke Jan Ijspeert.
Development of Human Locomotion
Robot design-- Four legged walking robot Instructors: Dr. A
Fuzzy control of a mobile robot Implementation using a MATLAB-based rapid prototyping system.
Biped Robots. Definitions Static Walking Static Walking The centre of gravity of the robot is always within the area bounded by the feet that are touching.
Instructor: Adi Hanuka By: Alon Berger Maor Itzhak Spring Semester 2014.
Microcontroller Robot Design Spring 2003 Advisor : Prof. Hayler Engineering Team: Mark Vo Jing Hua Zhong Abbas Ziadi.
Sensing self motion Key points: Why robots need self-sensing Sensors for proprioception in biological systems in robot systems Position sensing Velocity.
Progress Report Yoonsang Lee, Movement Research Lab., Seoul National University.
Locomotion control for a quadruped robot based on motor primitives Verena Hamburger.
Brian Renzenbrink Jeff Robble Object Tracking Using the Extended Kalman Particle Filter.
20/10/2009 IVR Herrmann IVR: Introduction to Control OVERVIEW Control systems Transformations Simple control algorithms.
Introduction to Computer Vision and Robotics: Motion Generation
International Conference on Sustainable Built Environment NANCO AND UNIVERSITY OF MELBOURNE JOINT RESEARCH SESSION ON NANOTECHNOLOGY AND SUSTAINABLE BUILT.
ROBOTICS.
Technical & Expressive Nature of Dance Technical Nature.
Whitman and Atkeson.  Present a decoupled controller for a simulated three-dimensional biped.  Dynamics broke down into multiple subsystems that are.
Quadruped Robot Modeling and Numerical Generation of the Open-Loop Trajectory Introduction We model a symmetric quadruped gait for a planar robot with.
Muhammad Al-Nasser Mohammad Shahab Stochastic Optimization of Bipedal Walking using Gyro Feedback and Phase Resetting King Fahd University of Petroleum.
Control 1 Keypoints: The control problem Forward models: –Geometric –Kinetic –Dynamic Process characteristics for a simple linear dynamic system.
Chapter 2 Hande AKA. Outline Agents and Environments Rationality The Nature of Environments Agent Types.
Circular Motion. PhET Lady Bug Motion Think about this Click “Show Both” at the top, and “Circular” at the bottom Watch the following and comment: Which.
A LONGITUDINAL EXAMINATION OF THE EMERGENCE OF A HEALTHY CHAOTIC WALKING PATTERN IN NORMAL INFANT DEVELOPMENT Harbourne, R.T. 1, Kurz, M. 2, and DeJong,
University of Windsor School of Computer Science Topics in Artificial Intelligence Fall 2008 Sept 11, 2008.
ZMP-BASED LOCOMOTION Robotics Course Lesson 22.
A RANS Based Prediction Method of Ship Roll Damping Moment Kumar Bappaditya Salui Supervisors of study: Professor Dracos Vassalos and Dr. Vladimir Shigunov.
Evolutionary Robotics
Control 3 Keypoints: PID control
Control. 3 Motion Control (kinematic control) for mobile platform The objective of a kinematic controller is to follow a trajectory described by its position.
BLDC Motor Speed Control with RPM Display. Introduction BLDC Motor Speed Control with RPM Display  The main objective of this.
Robot Intelligence Technology Lab. 10. Complex Hardware Morphologies: Walking Machines Presented by In-Won Park
EV3 Programming: Moving and Turning CONFIDENTIAL © 2014 Cymer, LLC.
Robot Intelligence Technology Lab. Evolutionary Robotics Chapter 3. How to Evolve Robots Chi-Ho Lee.
DYNAMICS OF CITY BIKE SHARING NETWORKS Kasia Samson & Claudio Durastanti.
Introduction to Robots and the Mind - Sensors - Bert Wachsmuth & Michael Vigorito Seton Hall University.
Autonomous Dynamically Simulated Creatures for Virtual Environments Paul Urban Supervisor: Prof. Shaun Bangay Honours Project 2001.
Resistance Warm-up Introduction Resistance Ohm’s law Measuring resistance ? Factors affecting resistance Resistors The multimeter Check-point 3.
Evolving robot brains using vision Lisa Meeden Computer Science Department Swarthmore College.
Automatic control systems I. Nonlinearities in Control System
Lecture Rigid Body Dynamics.
Sérgio Ronaldo Barros dos Santos Cairo Lúcio Nascimento Júnior
J. Gonzalez-Gomez, E. Boemo
Dr. Juan González-Gómez
Linear Control Systems
6.1 Introduction to Chi-Square Space
Presentation transcript:

Locomotion Exploiting Body Dynamics - Semester Project - Student: Matteo de Giacomi Supervisor: Jonas Buchli

INTRODUCTION - Purpose of the project - The Puppy II robot - The CPG - Turning

Project objectives  Develop a stable and controllable galloping gait for a quadruped robot endowed with passive dynamics  Use of a CPG based on Hopf oscillators

Puppy II  4 hip motors  1 spring per knee (passive dynamics)  Sensors (inertia, touch, tortion, IR)  Parameters: Amplitude Frequency Center of rotation

CPG  Fully connected system  Matrix describing a galloping in this system: FLFR RLRR

Turning  CPG: generates the basic galloping gait  Turn: modifies the basic rythm so that the robot can turn  Actuate:“translates“ the obtained values in values consistent with the robot architecture. CPG Turn Actuate basic rythm feedback Complete behaviour

Turning – Setpoint control  Idea: modify the basic position of each leg with a small value FL FR RL RR +Δs+Δs - Δs + Δs - Δs

Turning – Amplitude Control  Idea: Increase the amplitude of movement of two ipsilateral legs and decrease the amplitude of their two opposites.

PERFORMED TESTS - Introduction - Straight Locomotion - Setpoint Control - Amplitude Control

General Framework  Variables influencing PuppyII‘s behaviour: Amplitude Frequency Centers of oscillation  Centers of rotation have been fixed: PuppyII tilted 15° to the front

Test 1: Straight Locomotion (1)  Measure of linear speed depending on Amplitude and Frequency  1 measure: space covered over 5 sec  5 measures per test

Test 1: Straight Locomotion (2)  Under certain limits in amplitude and frequency, locomotion is stable  Amplitude seems a good way to control the robot‘s speed

Videos: Straight Locomotion

Tests on Turning Behaviour (1)  Fixed camera 2.45m over the robot  Robot equipped with a red led on its back  Robot behaviour filmed for various parameters  Tracking of the robot (red spot)  Circle estimation in Matlab Estimation of the turning radius of the robot depending on the used parameters

Tests on turning behaviour (2)  Example of circle estimation on tracked trajectory

Video: Turning

Test 2: Setpoint Control  At almost every speed (amplitude) it‘s possible to obtain a good turning behaviour with a good variety of turning radius

Test 3: Amplitude Control  At high speed (amplitudes) the turning radius doesn‘t seem to be affected by the used parameter  At low speeds some localized peaks emerge: the robot CAN‘T turn there!

CONCLUSION - Discussion - Further works

Discussion  Amplitude is a good way to control the robot‘s speed in a range of values contrained by the enviroment and by the robot itself.  Setpoint control is a good way to precisely control the turning radius of the robot  Amplitude control permits large turns at high speeds. At low speed shows a strange behaviour. Feature of the used springs?

Further Works  Feedback can improve the gait?  Embed the turning part in the oscillators themself may be useful?  We fixed some parameters (frequency and setpoints). What happens if we change them?

THE END Thank you! Any Question?