The robotic arm emerges as a subject of paramount significance within the
industrial landscape, particularly in addressing the complexities of its
kinematics. A significant research challenge lies in resolving the inverse
kinematics of multiple degree of freedom (M-DOF) robotic arms. The
inverse kinematics of M-DOF robotic arms pose a challenging problem to
resolve, thus it involves consideration of singularities which affect the arm
robot movement. This study aims a novel approach utilizing deep
reinforcement learning (DRL) to tackle the inverse kinematic problem of the
6-DOF PUMA manipulator as a representative case within the M-DOF
manipulator. The research employs Jacobian matrix for the kinematics
system that can solve the singularity, and deep deterministic policy gradient
(DDPG) as the kinematics solver. This chosen technique offers enhancing
speed and ensuring stability. The results of inverse kinematic solution using
DDPG were experimentally validated on a 6-DOF PUMA arm robot. The
DDPG successfully solves inverse kinematic solution and avoids the
singularity with 1,000 episodes and yielding a commendable total reward of
1,018.
Rumpun Ilmu
Teknik Elektro
Bahasa Asli/Original Language
English
Level
Internasional
Status
Dokumen Karya
No
Judul
Tipe Dokumen
Aksi
1
document-2.pdf
[PAK] Full Dokumen
2
Universitas Gadjah Mada Mail - [IJAI] Editor Decision.pdf
[PAK] Bukti Korespondensi Penulis
3
1_ accepted.pdf
[PAK] Bukti Korespondensi Penulis
4
2_ accepted for published.pdf
Dokumen Pendukung Karya Ilmiah (Hibah, Publikasi, Penelitian, Pengabdian)
5
BUKTI KORESPONDENSI ARTIKEL JURNAL Internasional Q2_compressed.pdf
[PAK] Bukti Korespondensi Penulis
6
2_ Turnitin Inverse Kinematic Solution and Singularity Avoidance Using a Deep Deterministic Policy Gradient Approach.pdf
