Machine Learning-Based Motion Planning for Robots

DOI

10.2991/978-94-6239-648-7_44How to use a DOI?

Keywords

Robot motion planning; Combining machine learning; Deep reinforcement learning; Hybrid approaches; Safety issues

Abstract

Motion planning for robots is a crucial technology that enables autonomous navigation and the performance of complex tasks. Traditional methods have problems like low efficiency and poor adaptability in high-dimensional spaces and dynamic environments. In recent years, machine learning methods, including supervised learning, reinforcement learning, and unsupervised learning, have offered new approaches to planning robot movement. This paper conducts a systematic review of the literature. It analyzes the latest progress in using machine learning for robot motion planning. It examines the types of algorithms, their applications, and the technical challenges they present. The research focuses on combining deep reinforcement learning, supervised learning, and traditional planning methods. It investigates how these can be used in mobile robots, robotic arms, and self-driving vehicles. The paper evaluates the effectiveness of various techniques. It highlights issues such as high computational complexity, over-reliance on data, and safety concerns. It also suggests future research, such as developing lighter models and utilizing formal verification. This review aims to provide a comprehensive reference for research in the field of automation and offer theoretical support for practical applications.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yumin Shi
PY  - 2026
DA  - 2026/04/24
TI  - Machine Learning-Based Motion Planning for Robots
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 400
EP  - 408
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_44
DO  - 10.2991/978-94-6239-648-7_44
ID  - Shi2026
ER  -