Study on optimization of trajectory of picking ARM robot in TOSHIBA DC650B machine system
Year: 2025
Type: article
Abstract: The evolution of the modern manufacturing sector has ushered in a new era of intricate applications for industrial robots. The traditional manual programming of these robots demands a significant investment of time and energy. Hence, the realm of automatic path planning has emerged as a prominent avenue for the advancement of industrial robotics. This article introduces a novel approach aimed at crafting the most efficient trajectory for a picking arm robot tasked with lifting 1 kg molded products from a TOSHIBA DC650B horizontal pressure casting machine. Our proposed solution involves harnessing the capabilities of the Rapidly-exploring Random Tree (RRT) algorithm alongside its refined counterpart, RRT*, to chart the optimal path for the robot to traverse towards its target destination within the workspace while avoiding collisions. The findings of our study underscore a substantial 26 % reduction in path length when implementing the RRT* algorithm in comparison to the original RRT algorithm. Furthermore, the employment of the RRT* algorithm not only enhances the efficiency of the robot's trajectory but also contributes to an overall improvement in performance metrics, cost-effectiveness, and safety standards within industrial production environments. This innovative methodology exhibits a broad spectrum of applicability, offering a streamlined programming approach that not only saves valuable time but also bolsters automation efficiency across diverse industrial sectors. By leveraging the advanced capabilities of the RRT* algorithm, industrial processes can benefit from optimized path planning, leading to enhanced operational performance and a reduction in overhead costs. As a result, this methodological approach promises significant improvements in the realm of industrial automation while simultaneously paving the way for future advancements in robotic applications
Source: EUREKA Physics and Engineering
Authors: Tien-Sy Nguyen, Van-Long Trinh, Thi-Thieu-Thoa Pham, Viet-Hoi Tran, Ngoc-Tien Tran
Institution: Hanoi University of Industry
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $161
OpenAlex ID: W4409044858