Biped Robot Walking using Particle Swarm Optimization Behzad Nikbin, Mohammad Reza Ranjbar, Behrooz Shafiee Sarjaz, Hamed Shah-Hosseini Faculty of Electrical.

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

Table of Contents Introduction to NAO Parameters of Walking Algorithm Walking Algorithm Optimization Results Future Works References

NAO, a Humanoid Robot

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

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

Parameters of Walking Algorithm Parameter1: Hip_Height the height of the hip joint during the walk process

Parameters of Walking Algorithm(2) Parameter2: Hip_Offset the distance along X-axis between left hip and left ankle.

Parameters of Walking Algorithm(3) Parameter3: Max_Ankle_Height the height of ankle joint at the highest point.

Parameters of Walking Algorithm(4) Parameter4: Num_of_Steps the number of cycles (hardwares clock cycle) necessary to complete a Half-Loop.

Parameters of Walking Algorithm(5) Parameter5: Gait_Length the distance traversed during a Half-Loop

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

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

Initial Values for Particles

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.

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

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

Fitness function Support Area: the area between the two legs

Fitness Function (2)

Simulation

Results Best and Average fitness of 10 run, at each iteration

Results (2) Positions of left and right leg during the walk

Results (3) Angles of each joint

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

Future works Omni-directional walking Using more DOFs (Degree of Freedom) of NAO to have more stable and faster walking

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)

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.

Biped Robot Walking using Particle Swarm Optimization Behzad Nikbin, Mohammad Reza Ranjbar, Behrooz Shafiee Sarjaz, Hamed Shah-Hosseini Faculty of Electrical.

Documents

simulation slide

iran slide

halfloop slide

humanoid robot slide

joints position slide

walk process slide

walking algorithm nao

given angular velocity