Research on robot path planning methods

Research Article
Open access

Research on robot path planning methods

Tianhao Chen 1* , Guanhong Jiang 2
  • 1 Shanghai University    
  • 2 Hong Kong Baptist University    
  • *corresponding author chentianhao1106@shu.edu.cn
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/15/20230807
ACE Vol.15
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-021-9​
ISBN (Online): 978-1-83558-022-6

Abstract

Mobile robots are used extensively across a variety of industries and fields. How to discover a start-to-end path without colliding has become a hot topic in recent years due to the complexity and uncertainty of the workplace. In various environments, a path planning technique should demonstrate high efficiency and speed. And this can reduce the energy consumption of the robots and greatly increase their working efficiency. This paper will conclude the presently popular path planning algorithm. Based on the different features of these algorithms, they are divided into three types: traditional path planning algorithm, neural-work-based algorithm, and sampling-based algorithm. Based on the new papers in these years, detailed introduction of the algorithms and their variants will be given. At the end of the paper, the thesis is summarized and the future research trend is prospected.

Keywords:

path planning, traditional algorithm, neural-work-based algorithm, sampling-based algorithm

Chen,T.;Jiang,G. (2023). Research on robot path planning methods. Applied and Computational Engineering,15,30-37.
Export citation

References

[1]. Tang, Z., & Ma, H. An overview of path planning algorithms. 2021 Earth and Environmental Science. 804 (2), p. 022024.

[2]. Li, X., Hu, X., Wang, Z., & Du, Z. Path planning based on combination of improved A-STAR algorithm and DWA algorithm. 2020 International Conference on Artificial Intelligence and Advanced Manufacture, 99-103.

[3]. Boots, B., Sugihara, K., Chiu, S. N., & Okabe, A. Spatial tessellations: concepts and applications of Voronoi diagrams 2009, John Wiley & Sons.

[4]. Chen, W., Wang, N., Liu, X., & Yang, C. VFH based local path planning for mobile robot. In 2019 China Symposium on Cognitive Computing and Hybrid Intelligence, 18-23.

[5]. Xu, Q. L., Yu, T., & Bai, J. The mobile robot path planning with motion constraints based on Bug algorithm. 2017 Chinese Automation Congress, 2348-2352.

[6]. Chen, Y. B., Luo, G. C., Mei, Y. S., Yu, J. Q., & Su, X. L. UAV path planning using artificial potential field method updated by optimal control theory. 2016 International Journal of Systems Science, 47(6), 1407-1420.

[7]. Zeng, J.; Qin, L.; Hu, Y.; Hu, C.; Yin, Q. Combining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder. 2019 Application. Science, 9, 323.

[8]. Lee, GyeongTaek. A Fully Controllable Agent in the Path Planning using Goal-Conditioned Reinforcement Learning. 2022 10.48550/arXiv.2205.09967.

[9]. Cao, Y., Hou, T., Wang, Y., Yi, X., & Sartoretti, G. ARiADNE: A Reinforcement learning approach using Attention-based Deep Networks for Exploration. 2023 ArXiv, abs/2301.11575.

[10]. Y. Zhang, J. Zhao and J. Sun, Robot Path Planning Method Based on Deep Reinforcement Learning, 2020 International Conference on Computer and Communication Engineering Technology, 49-53.

[11]. Z. Shen, P. Agrawal, J. P. Wilson, R. Harvey and S. Gupta, CPPNet: A Coverage Path Planning Network, 2021 OCEANS. 1-5.

[12]. J. Liu, B. Li, T. Li, W. Chi, J. Wang and M. Q.H. Meng, Learning-based Fast Path Planning in Complex Environments, 2021 IEEE International Conference on Robotics and Biomimetics. 1351-1358.

[13]. E. M. Ahmed, H. E. Abd El Munim and H. M. Shehata Bedour, An Accelerated Path Planning Approach, 2018 International Conference on Computer Engineering and Systems, 15-20.

[14]. A. A. Ravankar, T. Emaru and Y. Kobayashi, HPPRM: Hybrid Potential Based Probabilistic Roadmap Algorithm for Improved Dynamic Path Planning of Mobile Robots, 2020 IEEE Access, 8 221743-221766.

[15]. D. Armstrong and A. Jonasson, AM-RRT*: Informed Sampling-based Planning with Assisting Metric, 2021 IEEE International Conference on Robotics and Automation, Xi'an, 10093-10099.

[16]. Pedram, Ali Reza & Tanaka, Takashi. A Smoothing Algorithm for Minimum Sensing Path Plans in Gaussian Belief Space. 2023 IEEE Transactions on Robotics 32(5).

Cite this article

Chen,T.;Jiang,G. (2023). Research on robot path planning methods. Applied and Computational Engineering,15,30-37.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN：978-1-83558-021-9​(Print) / 978-1-83558-022-6(Online)
Editor：Marwan Omar, Roman Bauer, Alan Wang
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.15
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. Tang, Z., & Ma, H. An overview of path planning algorithms. 2021 Earth and Environmental Science. 804 (2), p. 022024.

[2]. Li, X., Hu, X., Wang, Z., & Du, Z. Path planning based on combination of improved A-STAR algorithm and DWA algorithm. 2020 International Conference on Artificial Intelligence and Advanced Manufacture, 99-103.

[3]. Boots, B., Sugihara, K., Chiu, S. N., & Okabe, A. Spatial tessellations: concepts and applications of Voronoi diagrams 2009, John Wiley & Sons.

[4]. Chen, W., Wang, N., Liu, X., & Yang, C. VFH based local path planning for mobile robot. In 2019 China Symposium on Cognitive Computing and Hybrid Intelligence, 18-23.

[5]. Xu, Q. L., Yu, T., & Bai, J. The mobile robot path planning with motion constraints based on Bug algorithm. 2017 Chinese Automation Congress, 2348-2352.

[6]. Chen, Y. B., Luo, G. C., Mei, Y. S., Yu, J. Q., & Su, X. L. UAV path planning using artificial potential field method updated by optimal control theory. 2016 International Journal of Systems Science, 47(6), 1407-1420.

[7]. Zeng, J.; Qin, L.; Hu, Y.; Hu, C.; Yin, Q. Combining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder. 2019 Application. Science, 9, 323.

[8]. Lee, GyeongTaek. A Fully Controllable Agent in the Path Planning using Goal-Conditioned Reinforcement Learning. 2022 10.48550/arXiv.2205.09967.

[9]. Cao, Y., Hou, T., Wang, Y., Yi, X., & Sartoretti, G. ARiADNE: A Reinforcement learning approach using Attention-based Deep Networks for Exploration. 2023 ArXiv, abs/2301.11575.

[10]. Y. Zhang, J. Zhao and J. Sun, Robot Path Planning Method Based on Deep Reinforcement Learning, 2020 International Conference on Computer and Communication Engineering Technology, 49-53.

[11]. Z. Shen, P. Agrawal, J. P. Wilson, R. Harvey and S. Gupta, CPPNet: A Coverage Path Planning Network, 2021 OCEANS. 1-5.

[12]. J. Liu, B. Li, T. Li, W. Chi, J. Wang and M. Q.H. Meng, Learning-based Fast Path Planning in Complex Environments, 2021 IEEE International Conference on Robotics and Biomimetics. 1351-1358.

[13]. E. M. Ahmed, H. E. Abd El Munim and H. M. Shehata Bedour, An Accelerated Path Planning Approach, 2018 International Conference on Computer Engineering and Systems, 15-20.

[14]. A. A. Ravankar, T. Emaru and Y. Kobayashi, HPPRM: Hybrid Potential Based Probabilistic Roadmap Algorithm for Improved Dynamic Path Planning of Mobile Robots, 2020 IEEE Access, 8 221743-221766.

[15]. D. Armstrong and A. Jonasson, AM-RRT*: Informed Sampling-based Planning with Assisting Metric, 2021 IEEE International Conference on Robotics and Automation, Xi'an, 10093-10099.

[16]. Pedram, Ali Reza & Tanaka, Takashi. A Smoothing Algorithm for Minimum Sensing Path Plans in Gaussian Belief Space. 2023 IEEE Transactions on Robotics 32(5).

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.