A survey of learning‐based robot motion planning. Issue 4 (21st May 2021)

• Record Type: Journal Article
• Authors: Wang, Jiankun; Zhang, Tianyi; Ma, Nachuan; Li, Zhaoting; Ma, Han; Meng, Fei; Meng, Max Q.‐H.
• Other Names: Zhang Yu guestEditor.; Gao Fei guestEditor.; Sun Yuxiang guestEditor.; Hovakimyan Naira guestEditor.; Fang Zheng guestEditor.
• Is Part Of: IET cyber-systems and robotics. Volume 3:Issue 4(2021)
• Page Start: 302
• Page End: 314
• Publication Date: 2021-05-21
• Subjects: Robotics -- Periodicals; Cybernetics -- Periodicals; Cybernetics; Robotics; Periodicals; 629
• DOI: 10.1049/csy2.12020
• Languages: English
• ISSNs: 2631-6315

Abstract

Abstract: A fundamental task in robotics is to plan collision‐free motions among a set of obstacles. Recently, learning‐based motion‐planning methods have shown significant advantages in solving different planning problems in high‐dimensional spaces and complex environments. This article serves as a survey of various different learning‐based methods that have been applied to robot motion‐planning problems, including supervised, unsupervised learning, and reinforcement learning. These learning‐based methods either rely on a human‐crafted reward function for specific tasks or learn from successful planning experiences. The classical definition and learning‐related definition of motion‐planning problem are provided in this article. Different learning‐based motion‐planning algorithms are introduced, and the combination of classical motion‐planning and learning techniques is discussed in detail.