SlideShare a Scribd company logo
Turning Robot Locomotion Using
    Truncated Fourier Series
               and
 Gravitational Search Algorithm

     L.ZARBAN, S. JAFARIZADEH, S. JAFARI
Subjects:
  History
  Walk Algorithm
      TFS
      Turn Algorithm
  GSA Algorithm
       GSA Flowchart Algorithm
       How to use
  Implementation Environment
  Robot Structure
  Experimental RESULTS
  References
  Question
History
 Shinya Aoi and Kazuo Tsuchiya(2004) – controlled turning of a biped
  locomotion robot using nonlinear oscillators.
 Nakanishi et al in 2004 and Yang et al in 2006, made use of
  recorded human gaits as the reference for robot trajectory planning.
 Yang analyzed human locomotion and used TFS together with a
  ZMP stability indicator to prove that; TFS can generate suitable
  angular trajectories for controlling bipedal locomotion.
 Nima Shafii(2009) - Humanoid Clock-Turning Gait Synthesis based
  on Fourier Series And Genetic Algorithms
 New at this work:
      - Modeling
      - Train Algorithm
Walk Algorithm
  Humanoid walk :
     - Human gaits on flat-terrain recorded by VICON .
         The below points can be modeled by Fourier Series
Truncated Fourier Series
  Truncated Fourier Series (TFS)




  An unique TFS is used for every body joints using its
   Corresponding points value.
  The Fourier parameters must be changed to making a better
   walk.
  We use the GSA algorithm to change and found a better
   parameters for our own TFS.
Turn Algorithm
  As an experimental result , We found out that the turn action is
   only based on the 3 joints(the hip-role, the hip-yaw and ankle)
  Every leg joints value has a phases distance with another leg. It's
   shown below: (other joints is defined as a constant value)
GSA Algorithm
  Gravitational Search Algorithm (GSA) is a heuristic optimization
   that is inspired of the law of gravity and mass interactions.
  The searcher agents are a collection of masses which interact with
   each other based on the Newtonian gravity and the laws of
   motion.
  In this algorithm, each agent has two characteristic, the position
   which is a solution for the problem and the mass which
   correspond to the performance of the solution.
  A heavy mass has a large effective attraction radius and hence a
   great intensity of attraction. Therefore, agents with a higher
   performance have a greater gravitational mass.
  As a result, the agents tend to move toward the best agent.
GSA Flowchart Algorithm
    (a) Search space identification.
    (b) Randomized initialization.
    (c) Fitness evaluation of agents.
    (d) Update G(t), best(t),
          worst(t) and Mi(t) for i = 1,2,. . .,N.
    (e) Calculation of the total force
          in different directions.
    (f) Calculation of acceleration
         and velocity.
    (g) Updating agents’ position.
    (h) Repeat steps c to g until the
          stop criteria is reached.
    (i) End.
How to Use
  As previous section, We know that only 2 equations has been
   created by Fourier series, then every two equation has a below
   parameters:
     - A0 , A1, B1, A2, B2, A3, B3 (Of The Fourier Series Equation)
  Modeling: Every agent is modeled as above collection set.(For
   every equation separately )
  Fitness Parameters: Constance distance from ball, Time to run
  Algorithm basic parameters:
     - population size : 50
     - Dimension : 11
     - maximum iteration : 100
Implementation Environment

  The robot in this study is a simulated model of NAO
   that is a real humanoid Robot with two arms, two legs
   and a head.
  we have implemented and tested our new optimizing
   approach on simulated NAO robot by an application
   called rcssserver3D.

  The robot skeleton is shown at the figure:
Robot Structure
Experimental RESULTS
  The angular trajectories for hip-yaw-pitch and hip roll joints of left
   leg is shown below. (Left leg is support in [t0,t1] and[t3,t4] and it is
   turning in [t1,t2]. In [t2,t3] both of legs on floor and the time the
   legs are switching.)

      Hip-roll joint




      Hip-yaw-pitch joint
Experimental RESULTS
  The robot can be turn and never falling if there is NOT external
   force.
  The below data diagrams is taken from experimental environment
   (Balance means usage of the Hip-Roll and Ankle-Roll joints only):
References
  S. Kajita, et al., Biped Walking Pattern Generation by using
   Preview Control of Zero-Moment Point, In: Proceedings of the
   2003 IEEE International Conference on Robotics & Automation
   Taipei, Taiwan, September 2003, pp. 14-19.
  S. Kajita, F. Kanehiro, K. Kaneko, K. Yokoi, H. Hirukawa. The 3D
   linear inverted pendulum mode A simple modeling for a biped
   walking pattern generation, In: Proceedings of the 2001 IEEE/RSJ
   International Conference on Intelligent Robots and Systems, 2001,
   pp. 239–246.
  Nima Shafii, Humanoid Clock-Turning Gait Synthesis based on
   Fourier Series And Genetic Algorithms , LIACC – Artificial
   Intelligence and Computer Science Lab., Porto, Portugal
References
  C. L. Shih, W. A. Gruver, and T.T Lee, Inverse kinematics and
   inverse dynamics for control of a biped walking machine, J. Robot.
   Syst. Vol.10, 1993, pp. 531-555.
  N. Shafii1, L. Paulo Reis, N. Lau, Biped Walking using Coronal
   and Sagittal Movements, based on Truncated Fourier Series,
   Proceedings of the 5th Doctoral Symposium in Informatics
   Engineering 2010
  G. Dip, V. Prahlad and P. Duc Kien, Genetic algorithm-based
   optimal bipedal walking gait synthesis considering tradeoff
   between stability margin and speed, Robotica, Vol. 27, 2009, pp.
   355-365.


  All of the references is listed at the paper.
Question



       Any Question?

More Related Content

What's hot

Particle Swarm Optimization
Particle Swarm OptimizationParticle Swarm Optimization
Particle Swarm OptimizationStelios Petrakis
 
Particle Swarm optimization
Particle Swarm optimizationParticle Swarm optimization
Particle Swarm optimization
midhulavijayan
 
Particle Swarm Optimization: The Algorithm and Its Applications
Particle Swarm Optimization: The Algorithm and Its ApplicationsParticle Swarm Optimization: The Algorithm and Its Applications
Particle Swarm Optimization: The Algorithm and Its Applications
adil raja
 
Particle swarm optimization
Particle swarm optimizationParticle swarm optimization
Particle swarm optimization
Mahesh Tibrewal
 
Particle Swarm Optimization
Particle Swarm OptimizationParticle Swarm Optimization
Particle Swarm Optimization
QasimRehman
 
Particle Swarm Optimization - PSO
Particle Swarm Optimization - PSOParticle Swarm Optimization - PSO
Particle Swarm Optimization - PSO
Mohamed Talaat
 
Ppt 13te89
Ppt 13te89Ppt 13te89
Particle swarm optimization
Particle swarm optimizationParticle swarm optimization
Particle swarm optimization
Suman Chatterjee
 
SI and PSO --Machine Learning
SI and PSO --Machine Learning SI and PSO --Machine Learning
SI and PSO --Machine Learning
Md. Shafiul Alam Sagor
 
[Paper Review] MIT Cheetah 1: Gait-pattern, trajectory generator
[Paper Review] MIT Cheetah 1: Gait-pattern, trajectory generator[Paper Review] MIT Cheetah 1: Gait-pattern, trajectory generator
[Paper Review] MIT Cheetah 1: Gait-pattern, trajectory generator
Hancheol Choi
 
Slotine adaptive control-manipulators
Slotine adaptive control-manipulatorsSlotine adaptive control-manipulators
Slotine adaptive control-manipulators
Hancheol Choi
 
Harmonic analysis of vehicle suspension system
Harmonic analysis of vehicle suspension systemHarmonic analysis of vehicle suspension system
Harmonic analysis of vehicle suspension system
Sanjeet Kumar Singh
 
Chapter 5 Powerpoint
Chapter 5 PowerpointChapter 5 Powerpoint
Chapter 5 Powerpoint
Mrreynon
 
A New Method For Solving Kinematics Model Of An RA-02
A New Method For Solving Kinematics Model Of An RA-02A New Method For Solving Kinematics Model Of An RA-02
A New Method For Solving Kinematics Model Of An RA-02
IJERA Editor
 
Glowworm Swarm Optimisation PPT
Glowworm Swarm Optimisation PPTGlowworm Swarm Optimisation PPT
Glowworm Swarm Optimisation PPT
Arijeet Satapathy
 
Firefly algorithm
Firefly algorithmFirefly algorithm
Firefly algorithm
Hasan Gök
 
Inverse Kinematics Using Genetic Algorithms
Inverse Kinematics Using Genetic AlgorithmsInverse Kinematics Using Genetic Algorithms
Inverse Kinematics Using Genetic Algorithms
satyendrajaladi
 
Glowworm swarm optimization (Presentation)
Glowworm swarm optimization (Presentation)Glowworm swarm optimization (Presentation)
Glowworm swarm optimization (Presentation)
Younis Al-Ibrahim
 

What's hot (19)

Particle Swarm Optimization
Particle Swarm OptimizationParticle Swarm Optimization
Particle Swarm Optimization
 
Particle Swarm optimization
Particle Swarm optimizationParticle Swarm optimization
Particle Swarm optimization
 
Particle Swarm Optimization: The Algorithm and Its Applications
Particle Swarm Optimization: The Algorithm and Its ApplicationsParticle Swarm Optimization: The Algorithm and Its Applications
Particle Swarm Optimization: The Algorithm and Its Applications
 
Particle swarm optimization
Particle swarm optimizationParticle swarm optimization
Particle swarm optimization
 
Particle Swarm Optimization
Particle Swarm OptimizationParticle Swarm Optimization
Particle Swarm Optimization
 
Particle Swarm Optimization - PSO
Particle Swarm Optimization - PSOParticle Swarm Optimization - PSO
Particle Swarm Optimization - PSO
 
Ppt 13te89
Ppt 13te89Ppt 13te89
Ppt 13te89
 
Particle swarm optimization
Particle swarm optimizationParticle swarm optimization
Particle swarm optimization
 
SI and PSO --Machine Learning
SI and PSO --Machine Learning SI and PSO --Machine Learning
SI and PSO --Machine Learning
 
[Paper Review] MIT Cheetah 1: Gait-pattern, trajectory generator
[Paper Review] MIT Cheetah 1: Gait-pattern, trajectory generator[Paper Review] MIT Cheetah 1: Gait-pattern, trajectory generator
[Paper Review] MIT Cheetah 1: Gait-pattern, trajectory generator
 
Slotine adaptive control-manipulators
Slotine adaptive control-manipulatorsSlotine adaptive control-manipulators
Slotine adaptive control-manipulators
 
gadget_meeting
gadget_meetinggadget_meeting
gadget_meeting
 
Harmonic analysis of vehicle suspension system
Harmonic analysis of vehicle suspension systemHarmonic analysis of vehicle suspension system
Harmonic analysis of vehicle suspension system
 
Chapter 5 Powerpoint
Chapter 5 PowerpointChapter 5 Powerpoint
Chapter 5 Powerpoint
 
A New Method For Solving Kinematics Model Of An RA-02
A New Method For Solving Kinematics Model Of An RA-02A New Method For Solving Kinematics Model Of An RA-02
A New Method For Solving Kinematics Model Of An RA-02
 
Glowworm Swarm Optimisation PPT
Glowworm Swarm Optimisation PPTGlowworm Swarm Optimisation PPT
Glowworm Swarm Optimisation PPT
 
Firefly algorithm
Firefly algorithmFirefly algorithm
Firefly algorithm
 
Inverse Kinematics Using Genetic Algorithms
Inverse Kinematics Using Genetic AlgorithmsInverse Kinematics Using Genetic Algorithms
Inverse Kinematics Using Genetic Algorithms
 
Glowworm swarm optimization (Presentation)
Glowworm swarm optimization (Presentation)Glowworm swarm optimization (Presentation)
Glowworm swarm optimization (Presentation)
 

Similar to Turning robot locomotion using truncated fourier series and gravitational search algorithm

Insect inspired hexapod robot for terrain navigation
Insect inspired hexapod robot for terrain navigationInsect inspired hexapod robot for terrain navigation
Insect inspired hexapod robot for terrain navigation
eSAT Journals
 
Insect inspired hexapod robot for terrain navigation
Insect inspired hexapod robot for terrain navigationInsect inspired hexapod robot for terrain navigation
Insect inspired hexapod robot for terrain navigation
eSAT Publishing House
 
Integral Backstepping Approach for Mobile Robot Control
Integral Backstepping Approach for Mobile Robot ControlIntegral Backstepping Approach for Mobile Robot Control
Integral Backstepping Approach for Mobile Robot Control
TELKOMNIKA JOURNAL
 
Gaitor final report
Gaitor final reportGaitor final report
Gaitor final report
Sifat Syed
 
A NOVEL NAVIGATION STRATEGY FOR AN UNICYCLE MOBILE ROBOT INSPIRED FROM THE DU...
A NOVEL NAVIGATION STRATEGY FOR AN UNICYCLE MOBILE ROBOT INSPIRED FROM THE DU...A NOVEL NAVIGATION STRATEGY FOR AN UNICYCLE MOBILE ROBOT INSPIRED FROM THE DU...
A NOVEL NAVIGATION STRATEGY FOR AN UNICYCLE MOBILE ROBOT INSPIRED FROM THE DU...
JaresJournal
 
A Bionic gait programming algorithm for Hexapod Robot
A Bionic gait programming algorithm for Hexapod RobotA Bionic gait programming algorithm for Hexapod Robot
A Bionic gait programming algorithm for Hexapod Robot
Hao Yuan Cheng
 
Stairs Detection Algorithm for Tri-Star Wheeled Robot and Experimental Valida...
Stairs Detection Algorithm for Tri-Star Wheeled Robot and Experimental Valida...Stairs Detection Algorithm for Tri-Star Wheeled Robot and Experimental Valida...
Stairs Detection Algorithm for Tri-Star Wheeled Robot and Experimental Valida...
Premier Publishers
 
ADAPTIVE TREADMILL CONTROL BY HUMAN WILL
ADAPTIVE TREADMILL CONTROL BY HUMAN WILLADAPTIVE TREADMILL CONTROL BY HUMAN WILL
ADAPTIVE TREADMILL CONTROL BY HUMAN WILLtoukaigi
 
Analytical Development of the Forward and Inverse Kinematics of A Robotic Leg...
Analytical Development of the Forward and Inverse Kinematics of A Robotic Leg...Analytical Development of the Forward and Inverse Kinematics of A Robotic Leg...
Analytical Development of the Forward and Inverse Kinematics of A Robotic Leg...
IJERA Editor
 
simuliton of biped walkinng robot using kinematics
simuliton of biped walkinng robot using kinematicssimuliton of biped walkinng robot using kinematics
simuliton of biped walkinng robot using kinematics
Reza Fazaeli
 
PSO APPLIED TO DESIGN OPTIMAL PD CONTROL FOR A UNICYCLE MOBILE ROBOT
PSO APPLIED TO DESIGN OPTIMAL PD CONTROL FOR A UNICYCLE MOBILE ROBOTPSO APPLIED TO DESIGN OPTIMAL PD CONTROL FOR A UNICYCLE MOBILE ROBOT
PSO APPLIED TO DESIGN OPTIMAL PD CONTROL FOR A UNICYCLE MOBILE ROBOT
JaresJournal
 
Modeling, Simulation, and Optimal Control for Two-Wheeled Self-Balancing Robot
Modeling, Simulation, and Optimal Control for Two-Wheeled Self-Balancing Robot Modeling, Simulation, and Optimal Control for Two-Wheeled Self-Balancing Robot
Modeling, Simulation, and Optimal Control for Two-Wheeled Self-Balancing Robot
IJECEIAES
 
Seth Hutchinson - Progress Toward a Robotic Bat
Seth Hutchinson -  Progress Toward a Robotic BatSeth Hutchinson -  Progress Toward a Robotic Bat
Seth Hutchinson - Progress Toward a Robotic Bat
Daniel Huber
 
Research.Essay_Chien-Chih_Weng_v3_by Prof. Karkoub
Research.Essay_Chien-Chih_Weng_v3_by Prof. KarkoubResearch.Essay_Chien-Chih_Weng_v3_by Prof. Karkoub
Research.Essay_Chien-Chih_Weng_v3_by Prof. KarkoubChien-Chih Weng
 
Manipulability index of a parallel robot manipulator
Manipulability index of a parallel robot manipulatorManipulability index of a parallel robot manipulator
Manipulability index of a parallel robot manipulator
IAEME Publication
 
Manipulability index of a parallel robot manipulator
Manipulability index of a parallel robot manipulatorManipulability index of a parallel robot manipulator
Manipulability index of a parallel robot manipulator
IAEME Publication
 
Research on The Control of Joint Robot Trajectory
Research on The Control of Joint Robot TrajectoryResearch on The Control of Joint Robot Trajectory
Research on The Control of Joint Robot Trajectory
IJRESJOURNAL
 

Similar to Turning robot locomotion using truncated fourier series and gravitational search algorithm (20)

Insect inspired hexapod robot for terrain navigation
Insect inspired hexapod robot for terrain navigationInsect inspired hexapod robot for terrain navigation
Insect inspired hexapod robot for terrain navigation
 
Insect inspired hexapod robot for terrain navigation
Insect inspired hexapod robot for terrain navigationInsect inspired hexapod robot for terrain navigation
Insect inspired hexapod robot for terrain navigation
 
Integral Backstepping Approach for Mobile Robot Control
Integral Backstepping Approach for Mobile Robot ControlIntegral Backstepping Approach for Mobile Robot Control
Integral Backstepping Approach for Mobile Robot Control
 
Gaitor final report
Gaitor final reportGaitor final report
Gaitor final report
 
A NOVEL NAVIGATION STRATEGY FOR AN UNICYCLE MOBILE ROBOT INSPIRED FROM THE DU...
A NOVEL NAVIGATION STRATEGY FOR AN UNICYCLE MOBILE ROBOT INSPIRED FROM THE DU...A NOVEL NAVIGATION STRATEGY FOR AN UNICYCLE MOBILE ROBOT INSPIRED FROM THE DU...
A NOVEL NAVIGATION STRATEGY FOR AN UNICYCLE MOBILE ROBOT INSPIRED FROM THE DU...
 
A Bionic gait programming algorithm for Hexapod Robot
A Bionic gait programming algorithm for Hexapod RobotA Bionic gait programming algorithm for Hexapod Robot
A Bionic gait programming algorithm for Hexapod Robot
 
Stairs Detection Algorithm for Tri-Star Wheeled Robot and Experimental Valida...
Stairs Detection Algorithm for Tri-Star Wheeled Robot and Experimental Valida...Stairs Detection Algorithm for Tri-Star Wheeled Robot and Experimental Valida...
Stairs Detection Algorithm for Tri-Star Wheeled Robot and Experimental Valida...
 
ADAPTIVE TREADMILL CONTROL BY HUMAN WILL
ADAPTIVE TREADMILL CONTROL BY HUMAN WILLADAPTIVE TREADMILL CONTROL BY HUMAN WILL
ADAPTIVE TREADMILL CONTROL BY HUMAN WILL
 
Analytical Development of the Forward and Inverse Kinematics of A Robotic Leg...
Analytical Development of the Forward and Inverse Kinematics of A Robotic Leg...Analytical Development of the Forward and Inverse Kinematics of A Robotic Leg...
Analytical Development of the Forward and Inverse Kinematics of A Robotic Leg...
 
simuliton of biped walkinng robot using kinematics
simuliton of biped walkinng robot using kinematicssimuliton of biped walkinng robot using kinematics
simuliton of biped walkinng robot using kinematics
 
PSO APPLIED TO DESIGN OPTIMAL PD CONTROL FOR A UNICYCLE MOBILE ROBOT
PSO APPLIED TO DESIGN OPTIMAL PD CONTROL FOR A UNICYCLE MOBILE ROBOTPSO APPLIED TO DESIGN OPTIMAL PD CONTROL FOR A UNICYCLE MOBILE ROBOT
PSO APPLIED TO DESIGN OPTIMAL PD CONTROL FOR A UNICYCLE MOBILE ROBOT
 
dheeraj
dheerajdheeraj
dheeraj
 
Modeling, Simulation, and Optimal Control for Two-Wheeled Self-Balancing Robot
Modeling, Simulation, and Optimal Control for Two-Wheeled Self-Balancing Robot Modeling, Simulation, and Optimal Control for Two-Wheeled Self-Balancing Robot
Modeling, Simulation, and Optimal Control for Two-Wheeled Self-Balancing Robot
 
Seth Hutchinson - Progress Toward a Robotic Bat
Seth Hutchinson -  Progress Toward a Robotic BatSeth Hutchinson -  Progress Toward a Robotic Bat
Seth Hutchinson - Progress Toward a Robotic Bat
 
Research.Essay_Chien-Chih_Weng_v3_by Prof. Karkoub
Research.Essay_Chien-Chih_Weng_v3_by Prof. KarkoubResearch.Essay_Chien-Chih_Weng_v3_by Prof. Karkoub
Research.Essay_Chien-Chih_Weng_v3_by Prof. Karkoub
 
Manipulability index of a parallel robot manipulator
Manipulability index of a parallel robot manipulatorManipulability index of a parallel robot manipulator
Manipulability index of a parallel robot manipulator
 
Manipulability index of a parallel robot manipulator
Manipulability index of a parallel robot manipulatorManipulability index of a parallel robot manipulator
Manipulability index of a parallel robot manipulator
 
Research on The Control of Joint Robot Trajectory
Research on The Control of Joint Robot TrajectoryResearch on The Control of Joint Robot Trajectory
Research on The Control of Joint Robot Trajectory
 
4260 9235-1-pb
4260 9235-1-pb4260 9235-1-pb
4260 9235-1-pb
 
Poster presentation
Poster presentationPoster presentation
Poster presentation
 

Recently uploaded

20240607 QFM018 Elixir Reading List May 2024
20240607 QFM018 Elixir Reading List May 202420240607 QFM018 Elixir Reading List May 2024
20240607 QFM018 Elixir Reading List May 2024
Matthew Sinclair
 
A tale of scale & speed: How the US Navy is enabling software delivery from l...
A tale of scale & speed: How the US Navy is enabling software delivery from l...A tale of scale & speed: How the US Navy is enabling software delivery from l...
A tale of scale & speed: How the US Navy is enabling software delivery from l...
sonjaschweigert1
 
GraphRAG is All You need? LLM & Knowledge Graph
GraphRAG is All You need? LLM & Knowledge GraphGraphRAG is All You need? LLM & Knowledge Graph
GraphRAG is All You need? LLM & Knowledge Graph
Guy Korland
 
Pushing the limits of ePRTC: 100ns holdover for 100 days
Pushing the limits of ePRTC: 100ns holdover for 100 daysPushing the limits of ePRTC: 100ns holdover for 100 days
Pushing the limits of ePRTC: 100ns holdover for 100 days
Adtran
 
Elevating Tactical DDD Patterns Through Object Calisthenics
Elevating Tactical DDD Patterns Through Object CalisthenicsElevating Tactical DDD Patterns Through Object Calisthenics
Elevating Tactical DDD Patterns Through Object Calisthenics
Dorra BARTAGUIZ
 
FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf
FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdfFIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf
FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf
FIDO Alliance
 
FIDO Alliance Osaka Seminar: Passkeys at Amazon.pdf
FIDO Alliance Osaka Seminar: Passkeys at Amazon.pdfFIDO Alliance Osaka Seminar: Passkeys at Amazon.pdf
FIDO Alliance Osaka Seminar: Passkeys at Amazon.pdf
FIDO Alliance
 
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionGenerative AI Deep Dive: Advancing from Proof of Concept to Production
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Aggregage
 
Secstrike : Reverse Engineering & Pwnable tools for CTF.pptx
Secstrike : Reverse Engineering & Pwnable tools for CTF.pptxSecstrike : Reverse Engineering & Pwnable tools for CTF.pptx
Secstrike : Reverse Engineering & Pwnable tools for CTF.pptx
nkrafacyberclub
 
DevOps and Testing slides at DASA Connect
DevOps and Testing slides at DASA ConnectDevOps and Testing slides at DASA Connect
DevOps and Testing slides at DASA Connect
Kari Kakkonen
 
Microsoft - Power Platform_G.Aspiotis.pdf
Microsoft - Power Platform_G.Aspiotis.pdfMicrosoft - Power Platform_G.Aspiotis.pdf
Microsoft - Power Platform_G.Aspiotis.pdf
Uni Systems S.M.S.A.
 
FIDO Alliance Osaka Seminar: Overview.pdf
FIDO Alliance Osaka Seminar: Overview.pdfFIDO Alliance Osaka Seminar: Overview.pdf
FIDO Alliance Osaka Seminar: Overview.pdf
FIDO Alliance
 
GridMate - End to end testing is a critical piece to ensure quality and avoid...
GridMate - End to end testing is a critical piece to ensure quality and avoid...GridMate - End to end testing is a critical piece to ensure quality and avoid...
GridMate - End to end testing is a critical piece to ensure quality and avoid...
ThomasParaiso2
 
Removing Uninteresting Bytes in Software Fuzzing
Removing Uninteresting Bytes in Software FuzzingRemoving Uninteresting Bytes in Software Fuzzing
Removing Uninteresting Bytes in Software Fuzzing
Aftab Hussain
 
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
James Anderson
 
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Neo4j
 
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfObservability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Paige Cruz
 
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing DaysClimate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
Kari Kakkonen
 
Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1
DianaGray10
 
Epistemic Interaction - tuning interfaces to provide information for AI support
Epistemic Interaction - tuning interfaces to provide information for AI supportEpistemic Interaction - tuning interfaces to provide information for AI support
Epistemic Interaction - tuning interfaces to provide information for AI support
Alan Dix
 

Recently uploaded (20)

20240607 QFM018 Elixir Reading List May 2024
20240607 QFM018 Elixir Reading List May 202420240607 QFM018 Elixir Reading List May 2024
20240607 QFM018 Elixir Reading List May 2024
 
A tale of scale & speed: How the US Navy is enabling software delivery from l...
A tale of scale & speed: How the US Navy is enabling software delivery from l...A tale of scale & speed: How the US Navy is enabling software delivery from l...
A tale of scale & speed: How the US Navy is enabling software delivery from l...
 
GraphRAG is All You need? LLM & Knowledge Graph
GraphRAG is All You need? LLM & Knowledge GraphGraphRAG is All You need? LLM & Knowledge Graph
GraphRAG is All You need? LLM & Knowledge Graph
 
Pushing the limits of ePRTC: 100ns holdover for 100 days
Pushing the limits of ePRTC: 100ns holdover for 100 daysPushing the limits of ePRTC: 100ns holdover for 100 days
Pushing the limits of ePRTC: 100ns holdover for 100 days
 
Elevating Tactical DDD Patterns Through Object Calisthenics
Elevating Tactical DDD Patterns Through Object CalisthenicsElevating Tactical DDD Patterns Through Object Calisthenics
Elevating Tactical DDD Patterns Through Object Calisthenics
 
FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf
FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdfFIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf
FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf
 
FIDO Alliance Osaka Seminar: Passkeys at Amazon.pdf
FIDO Alliance Osaka Seminar: Passkeys at Amazon.pdfFIDO Alliance Osaka Seminar: Passkeys at Amazon.pdf
FIDO Alliance Osaka Seminar: Passkeys at Amazon.pdf
 
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionGenerative AI Deep Dive: Advancing from Proof of Concept to Production
Generative AI Deep Dive: Advancing from Proof of Concept to Production
 
Secstrike : Reverse Engineering & Pwnable tools for CTF.pptx
Secstrike : Reverse Engineering & Pwnable tools for CTF.pptxSecstrike : Reverse Engineering & Pwnable tools for CTF.pptx
Secstrike : Reverse Engineering & Pwnable tools for CTF.pptx
 
DevOps and Testing slides at DASA Connect
DevOps and Testing slides at DASA ConnectDevOps and Testing slides at DASA Connect
DevOps and Testing slides at DASA Connect
 
Microsoft - Power Platform_G.Aspiotis.pdf
Microsoft - Power Platform_G.Aspiotis.pdfMicrosoft - Power Platform_G.Aspiotis.pdf
Microsoft - Power Platform_G.Aspiotis.pdf
 
FIDO Alliance Osaka Seminar: Overview.pdf
FIDO Alliance Osaka Seminar: Overview.pdfFIDO Alliance Osaka Seminar: Overview.pdf
FIDO Alliance Osaka Seminar: Overview.pdf
 
GridMate - End to end testing is a critical piece to ensure quality and avoid...
GridMate - End to end testing is a critical piece to ensure quality and avoid...GridMate - End to end testing is a critical piece to ensure quality and avoid...
GridMate - End to end testing is a critical piece to ensure quality and avoid...
 
Removing Uninteresting Bytes in Software Fuzzing
Removing Uninteresting Bytes in Software FuzzingRemoving Uninteresting Bytes in Software Fuzzing
Removing Uninteresting Bytes in Software Fuzzing
 
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
 
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
 
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfObservability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
 
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing DaysClimate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
 
Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1
 
Epistemic Interaction - tuning interfaces to provide information for AI support
Epistemic Interaction - tuning interfaces to provide information for AI supportEpistemic Interaction - tuning interfaces to provide information for AI support
Epistemic Interaction - tuning interfaces to provide information for AI support
 

Turning robot locomotion using truncated fourier series and gravitational search algorithm

  • 1. Turning Robot Locomotion Using Truncated Fourier Series and Gravitational Search Algorithm L.ZARBAN, S. JAFARIZADEH, S. JAFARI
  • 2. Subjects:  History  Walk Algorithm TFS Turn Algorithm  GSA Algorithm GSA Flowchart Algorithm How to use  Implementation Environment  Robot Structure  Experimental RESULTS  References  Question
  • 3. History  Shinya Aoi and Kazuo Tsuchiya(2004) – controlled turning of a biped locomotion robot using nonlinear oscillators.  Nakanishi et al in 2004 and Yang et al in 2006, made use of recorded human gaits as the reference for robot trajectory planning.  Yang analyzed human locomotion and used TFS together with a ZMP stability indicator to prove that; TFS can generate suitable angular trajectories for controlling bipedal locomotion.  Nima Shafii(2009) - Humanoid Clock-Turning Gait Synthesis based on Fourier Series And Genetic Algorithms  New at this work: - Modeling - Train Algorithm
  • 4. Walk Algorithm  Humanoid walk : - Human gaits on flat-terrain recorded by VICON . The below points can be modeled by Fourier Series
  • 5. Truncated Fourier Series  Truncated Fourier Series (TFS)  An unique TFS is used for every body joints using its Corresponding points value.  The Fourier parameters must be changed to making a better walk.  We use the GSA algorithm to change and found a better parameters for our own TFS.
  • 6. Turn Algorithm  As an experimental result , We found out that the turn action is only based on the 3 joints(the hip-role, the hip-yaw and ankle)  Every leg joints value has a phases distance with another leg. It's shown below: (other joints is defined as a constant value)
  • 7. GSA Algorithm  Gravitational Search Algorithm (GSA) is a heuristic optimization that is inspired of the law of gravity and mass interactions.  The searcher agents are a collection of masses which interact with each other based on the Newtonian gravity and the laws of motion.  In this algorithm, each agent has two characteristic, the position which is a solution for the problem and the mass which correspond to the performance of the solution.  A heavy mass has a large effective attraction radius and hence a great intensity of attraction. Therefore, agents with a higher performance have a greater gravitational mass.  As a result, the agents tend to move toward the best agent.
  • 8. GSA Flowchart Algorithm  (a) Search space identification.  (b) Randomized initialization.  (c) Fitness evaluation of agents.  (d) Update G(t), best(t), worst(t) and Mi(t) for i = 1,2,. . .,N.  (e) Calculation of the total force in different directions.  (f) Calculation of acceleration and velocity.  (g) Updating agents’ position.  (h) Repeat steps c to g until the stop criteria is reached.  (i) End.
  • 9. How to Use  As previous section, We know that only 2 equations has been created by Fourier series, then every two equation has a below parameters: - A0 , A1, B1, A2, B2, A3, B3 (Of The Fourier Series Equation)  Modeling: Every agent is modeled as above collection set.(For every equation separately )  Fitness Parameters: Constance distance from ball, Time to run  Algorithm basic parameters: - population size : 50 - Dimension : 11 - maximum iteration : 100
  • 10. Implementation Environment  The robot in this study is a simulated model of NAO that is a real humanoid Robot with two arms, two legs and a head.  we have implemented and tested our new optimizing approach on simulated NAO robot by an application called rcssserver3D.  The robot skeleton is shown at the figure:
  • 12. Experimental RESULTS  The angular trajectories for hip-yaw-pitch and hip roll joints of left leg is shown below. (Left leg is support in [t0,t1] and[t3,t4] and it is turning in [t1,t2]. In [t2,t3] both of legs on floor and the time the legs are switching.) Hip-roll joint Hip-yaw-pitch joint
  • 13. Experimental RESULTS  The robot can be turn and never falling if there is NOT external force.  The below data diagrams is taken from experimental environment (Balance means usage of the Hip-Roll and Ankle-Roll joints only):
  • 14. References  S. Kajita, et al., Biped Walking Pattern Generation by using Preview Control of Zero-Moment Point, In: Proceedings of the 2003 IEEE International Conference on Robotics & Automation Taipei, Taiwan, September 2003, pp. 14-19.  S. Kajita, F. Kanehiro, K. Kaneko, K. Yokoi, H. Hirukawa. The 3D linear inverted pendulum mode A simple modeling for a biped walking pattern generation, In: Proceedings of the 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2001, pp. 239–246.  Nima Shafii, Humanoid Clock-Turning Gait Synthesis based on Fourier Series And Genetic Algorithms , LIACC – Artificial Intelligence and Computer Science Lab., Porto, Portugal
  • 15. References  C. L. Shih, W. A. Gruver, and T.T Lee, Inverse kinematics and inverse dynamics for control of a biped walking machine, J. Robot. Syst. Vol.10, 1993, pp. 531-555.  N. Shafii1, L. Paulo Reis, N. Lau, Biped Walking using Coronal and Sagittal Movements, based on Truncated Fourier Series, Proceedings of the 5th Doctoral Symposium in Informatics Engineering 2010  G. Dip, V. Prahlad and P. Duc Kien, Genetic algorithm-based optimal bipedal walking gait synthesis considering tradeoff between stability margin and speed, Robotica, Vol. 27, 2009, pp. 355-365.  All of the references is listed at the paper.
  • 16. Question  Any Question?