Deep learning-based optimization for motion planning of dual-arm assembly robots. (October 2021)

• Record Type: Journal Article
• Authors: Ying, Kuo-Ching; Pourhejazy, Pourya; Cheng, Chen-Yang; Cai, Zong-Ying
• Is Part Of: Computers & industrial engineering. Volume 160(2021)
• Publication Date: 2021-10
• Subjects: Motion planning -- Optimization -- Dual-arm robots -- Random tree -- Long short-term memory -- Intelligent manufacturing; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2021.107603
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: An algorithm for planning collision-free trajectories of robots is developed. The developed algorithm reduces the path length at a short computational time. The approach outperforms in two- and three-dimensional operational environments. Abstract: With the rapid technological and economic development, a growing number of companies are employing robots for their production and service operations. Motion planning is a fundamental topic in robotics that has received wide attention due to its importance in the development of industry 4.0 and intelligent manufacturing systems. This study sought to develop a deep learning-based optimization algorithm for planning collision-free trajectories of dual-arm assembly robots in complex operational environments. Given the high dimensionality of the robotic motion patterns, a Bi-directional Rapidly-exploring Random Tree integrated with the Long Short-term Memory (LSTM-BiRRT) method is proposed to enhance the effectiveness and efficiency of the planning process. Numerical experiments demonstrated that the LSTM-BiRRT algorithm outperforms the state-of-the-art approaches developed for motion planning of dual-arm robots in both two- and three-dimensional environments. The developed algorithm reduces the path length of the robotic operations at a significantly shorter computational time. The LSTM-BiRRT algorithm can serve as a strong benchmark for future developments as well as applications in the process autonomy across intelligent supply chains.