Home // IARIA Congress 2025, The 2025 IARIA Annual Congress on Frontiers in Science, Technology, Services, and Applications // View article
Authors:
Jong-Chen Chen
Guan-Rong Chen
Keywords: sensors; artificial neural networks; computational intelligence; robot; autonomous learning.
Abstract:
With the widespread use of intelligent robots, robotic arms play an increasingly vital role across various fields. This study explores using a system endowed with autonomous learning capabilities to learn and control the movements of a six-axis robotic arm. The research method enables this robotic arm to autonomously determine its movement trajectory, transitioning from a specific point to a fixed position while grasping an object at a designated angle. This process involves managing the broader movement trajectories associated with the arm's operations and ensuring precise coordination for practical suction actions. The WLKATA Mirobot serves as the experimental testbed for this study; it is a compact six-axis machine designed for tabletop use. The primary control mechanism is linked to an artificial neuromolecular system developed earlier in this team, characterized by a closely aligned relationship between structure and function that evolves. This design facilitates continuous learning, allowing the robotic arm to accomplish assigned tasks without rigid time constraints. Various trajectories were established in the experiments, enabling the arm to navigate toward desired target points based on specific requirements. The results indicate that the system can successfully reach target points and effectively grasp objects. Additionally, thorough testing was conducted to evaluate whether the molecular-like nervous system allows the robotic arm to execute corresponding movements proficiently. The study shows that this molecular-like jumpy system can effectively utilize previously learned actions after a learning period. This adaptability enables the robotic arm to adjust its operations for similar tasks, thereby achieving what is known as the transfer learning effect.
Pages: 8 to 9
Copyright: Copyright (c) IARIA, 2025
Publication date: July 6, 2025
Published in: conference
ISBN: 978-1-68558-284-5
Location: Venice, Italy
Dates: from July 6, 2025 to July 10, 2025