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Zendi Iklima
Department of Electrical Engineering, Universitas Mercu Buana
Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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SELF-COLLISION AVOIDANCE OF ARM ROBOT USING GENERATIVE ADVERSARIAL NETWORK AND PARTICLES SWARM OPTIMIZATION (GAN-PSO) Zendi Iklima; Andi Adriansyah; Sabin Hitimana
SINERGI Vol 25, No 2 (2021)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2021.2.005

Abstract

Collision avoidance of Arm Robot is designed for the robot to collide objects, colliding environment, and colliding its body. Self-collision avoidance was successfully trained using Generative Adversarial Networks (GANs) and Particle Swarm Optimization (PSO). The Inverse Kinematics (IK) with 96K motion data was extracted as the dataset to train data distribution of  3.6K samples and 7.2K samples. The proposed method GANs-PSO can solve the common GAN problem such as Mode Collapse or Helvetica Scenario that occurs when the generator  always gets the same output point which mapped to different input  values. The discriminator  produces the random samples' data distribution in which present the real data distribution (generated by Inverse Kinematic analysis).  The PSO was successfully reduced the number of training epochs of the generator  only with 5000 iterations. The result of our proposed method (GANs-PSO) with 50 particles was 5000 training epochs executed in 0.028ms per single prediction and 0.027474% Generator Mean Square Error (GMSE).
Co-Authors Andi Adriansyah Sabin Hitimana