Biped Robot Walking using Particle Swarm Optimization Behzad
Nikbin, Mohammad Reza Ranjbar, Behrooz Shafiee Sarjaz, Hamed
Shah-Hosseini Faculty of Electrical and Computer Engineering,
Shahid Beheshti University, G.C., Tehran, Iran
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Table of Contents Introduction to NAO Parameters of Walking
Algorithm Walking Algorithm Optimization Results Future Works
References
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NAO, a Humanoid Robot
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NAOs technical information Clock rate 50 Hz (T=0.02s) Image
processing tools Height: 57 cm Pressure sensors Gravity sensor
Gyroscope 23 DOFs Each joint moves with a given angular
velocity
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Simulated NAO Optimization algorithms can run it again and
again easily, and without wasting too much time No cost Standard
simulated NAO is used in RoboCup Soccer 3D Simulation league
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Parameters of Walking Algorithm Parameter1: Hip_Height the
height of the hip joint during the walk process
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Parameters of Walking Algorithm(2) Parameter2: Hip_Offset the
distance along X-axis between left hip and left ankle.
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Parameters of Walking Algorithm(3) Parameter3: Max_Ankle_Height
the height of ankle joint at the highest point.
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Parameters of Walking Algorithm(4) Parameter4: Num_of_Steps the
number of cycles (hardwares clock cycle) necessary to complete a
Half-Loop.
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Parameters of Walking Algorithm(5) Parameter5: Gait_Length the
distance traversed during a Half-Loop
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Walking Algorithm NAO needs the angular velocity of each joint
at each cycle The position of each joint at each cycle, can be
calculated inverse kinematics The angular velocity of each joint at
each cycle, would be calculated according to the joints
position
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Optimization An optimization algorithm needed to optimize the
five introduced parameters Proper initial values A visual toolkit
is developed to manipulate the parameters and test them Particle
Swarm Optimization algorithm Open source PSO library (jSwarm) is
utilized
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Initial Values for Particles
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Particle Swarm Optimization (PSO) PSO is a robust stochastic
optimization technique based on the movement and intelligence of
swarms. PSO applies the concept of social interaction to problem
solving. It was developed in 1995 by James Kennedy
(social-psychologist) and Russell Eberhart (electrical engineer).
It uses a number of agents (particles) that constitute a swarm
moving around in the search space looking for the best solution.
Each particle is treated as a point in a N-dimensional space which
adjusts its flying according to its own flying experience as well
as the flying experience of other particles.
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Particle Swarm Optimization jSwarm Open source java library for
PSO No. of Iteratio ns No. of Particle s InertiaParticle Increme nt
Global Increme nt 30200.980.01
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Limiting the Sample Space By limiting the parameters, an
optimization algorithm such as PSO, will converge much faster Joint
nameLower bound Upper bound Hip_Height11 cm25 cm Hip_Offset1 cm10
cm Max_Ankle_Height1 mm4 cm Steps314 Gait_Length1 cm30 cm
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Fitness function Support Area: the area between the two
legs
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Fitness Function (2)
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Simulation
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Simulation
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Results Best and Average fitness of 10 run, at each
iteration
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Results (2) Positions of left and right leg during the
walk
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Results (3) Angles of each joint
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Results (4) The best particle: Speed: 0.62 m/s Gait_Leng th No.
of steps Max_Ankle_Heigh t Hip_Offse t Hip_Heig ht 19.3 cm83.0
cm8.3 cm17.9 cm
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Future works Omni-directional walking Using more DOFs (Degree
of Freedom) of NAO to have more stable and faster walking
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References W. T. Miller III: Real-time neural network control
of a biped walking robot. IEEE Control Systems Magazine. 14(1):
41-48 (1994) C. L. Shih: Ascending and descending stairs for a
biped robot. IEEE Transactions on Systems, Man, andCybernetics.
29(3): 255-268 (1999) J. Yamaguchi, E. Soga, S. Inoue, A.
Takanishi: Development of a bipedal humanoid robot control method
of whole body cooperative dynamic biped walking. Paper presented at
the IEEE international conference on robotics and automation,
Detroit, Michigan, 10-15 May 1999 K. Hirai, M. Hirose, T. Takenaka:
The development of Honda humanoid robot. Paper presented at the
IEEE international conference on robotics and automation, Leuven,
Belgium, 16-20 May 1998 S. Kajita, F. Kanehiro, K. Kaneko, K.
Fujiwara, K. Harada, K. Yokoi, H. Hirukawa: Biped walking pattern
generation by using preview control of Zero-Moment Point. Paper
presented at the IEEE international conference on robotics and
automation, Taipei, Taiwan, 14-19 Sep. 2003 M.Vukobratovic, B.
Borovac, D. Surla, D. Stokic: Biped locomotion. Springer-Verlag
(1990)
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References (2) K. Nishiwaki, S. Kagami, Y. Kuniyoshi, M. Inaba,
H. Inoue: Online generation of humanoid walking motion based on
fast generation method of motion pattern that follows desired ZMP.
Paper presented at the IEEE/RSJ international conference on
intelligent robots and systems, Lausanne, Switzerland, 30 Sep. 5
Oct. 2002 J. Mrozowski, J. Awrejcewicz, P. Bamberski: Analysis of
stability of the human gait, Journal of Theoretical and Applied
Mechanics, vol. 45, no. 1, pp. 91-98, 2007 L. Yang, C. Chew, T.
Zielinska, A. Poo:A uniform biped gait generator with offline
optimization and online adjustable parameters, Robotica (2007)
volume 25, pp. 549565, March 19, 2007 JSwarm,
http://jswarm-pso.sourceforge.net, Last Access Oct.
2011.http://jswarm-pso.sourceforge.net N. Shafii, S. Aslani,
S.Mohammad, H.S.Javadi,V. Azizi, O. M. Nezami: Robust Humanoid
walking using Truncated Fourier Series gait generator,Iran Open
Symposium, April. 2009 N. Shafii, L.P. Reis, N. Lau: Biped Walking
using Coronal and Sagittal Movements based on Truncated Fourier
Series, RoboCup-2010: Robot Soccer World Cup XIII, Springer LNAI /
LNCS, Vol. 6556, pp. 324-335, Berlin, 2011.