Imperial College London
Alternate

ProfessorPaulKelly

Faculty of Engineering, Department of Computing

Professor of Software Technology
 
 
 
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Contact

 

+44 (0)20 7594 8332p.kelly Website

 
 
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Location

 

Level 3 (upstairs), William Penney Building, room 304William Penney LaboratorySouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Hsueh:2022:10.1080/01691864.2022.2062259,
author = {Hsueh, H-Y and Toma, A-I and Jaafar, HA and Stow, E and Murai, R and Kelly, PHJ and Saeedi, S},
doi = {10.1080/01691864.2022.2062259},
journal = {Advanced Robotics},
pages = {566--581},
title = {Systematic comparison of path planning algorithms using PathBench},
url = {http://dx.doi.org/10.1080/01691864.2022.2062259},
volume = {36},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Path planning is an essential component of mobile robotics. Classical path planning algorithms, such as wavefront and rapidly exploring random tree, are used heavily in autonomous robots. With the recent advances in machine learning, development of learning-based path planning algorithms has been experiencing a rapid growth. A unified path planning interface that facilitates the development and benchmarking of existing and new algorithms is needed. This paper presents PathBench, a platform for developing, visualizing, training, testing, and benchmarking of existing and future, classical and learning-based path planning algorithms in 2D and 3D grid world environments. Many existing path planning algorithms are supported, e.g. A, Dijkstra, waypoint planning networks, value iteration networks, and gated path planning networks; integrating new algorithms is easy and clearly specified. The benchmarking ability of PathBench is explored in this paper by comparing algorithms across five different hardware systems and three different map types, including built-in PathBench maps, video game maps, and maps from real world databases. Metrics, such as path length, success rate, and computational time, were used to evaluate algorithms. Algorithmic analysis was also performed on a real-world robot to demonstrate PathBench's support for Robot Operating System. PathBench is open source1.
AU  - Hsueh,H-Y
AU  - Toma,A-I
AU  - Jaafar,HA
AU  - Stow,E
AU  - Murai,R
AU  - Kelly,PHJ
AU  - Saeedi,S
DO  - 10.1080/01691864.2022.2062259
EP  - 581
PY  - 2022///
SN  - 0169-1864
SP  - 566
TI  - Systematic comparison of path planning algorithms using PathBench
T2  - Advanced Robotics
UR  - http://dx.doi.org/10.1080/01691864.2022.2062259
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000788825800001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://www.tandfonline.com/doi/full/10.1080/01691864.2022.2062259
UR  - http://hdl.handle.net/10044/1/98216
VL  - 36
ER  -
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