Open AccessA survey of learning‐based robot motion planning
Author(s) -
Wang Jiankun,
Zhang Tianyi,
Ma Nachuan,
Li Zhaoting,
Ma Han,
Meng Fei,
Meng Max Q.H.
Publication year - 2021
Publication title -
iet cyber‐systems and robotics
Language(s) - English
Resource type - Journals
ISSN - 2631-6315
DOI - 10.1049/csy2.12020
Subject(s) - motion planning , motion (physics) , reinforcement learning , artificial intelligence , computer science , plan (archaeology) , set (abstract data type) , robot learning , task (project management) , robot , function (biology) , robotics , unsupervised learning , machine learning , supervised learning , engineering , mobile robot , artificial neural network , geography , archaeology , systems engineering , evolutionary biology , biology , programming language
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.