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Systematic comparison of path planning algorithms using PathBench
Hsueh, Hao-Ya ; Toma, Alexandru-Iosif ; Jaafar, Hussein Ali ; Stow, Edward ; Murai, Riku ; Kelly, Paul H. J. ; Saeedi, Sajad
Taylor & Francis 2022
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Título:
Systematic comparison of path planning algorithms using PathBench
Autor:
Hsueh, Hao-Ya
;
Toma, Alexandru-Iosif
;
Jaafar, Hussein Ali
;
Stow, Edward
;
Murai, Riku
;
Kelly, Paul H. J.
;
Saeedi, Sajad
Assuntos:
Biological Sciences not elsewhere classified
;
FOS: Biological sciences
;
FOS: Computer and information sciences
;
FOS: Mathematics
;
Information Systems not elsewhere classified
;
Mathematical Sciences not elsewhere classified
;
Science Policy
;
Space Science
Notas:
10.1080/01691864.2022.2062259
RelationTypeNote: IsSupplementTo -- 10.1080/01691864.2022.2062259
Descrição:
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.
Editor:
Taylor & Francis
Data de criação/publicação:
2022
Idioma:
Inglês
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