Computer Science > Robotics
arXiv:2309.14845 (cs)
[Submitted on 26 Sep 2023 (v1), last revised 22 Nov 2023 (this version, v2)]
Title:Graph Neural Network Based Method for Path Planning Problem
View a PDF of the paper titled Graph Neural Network Based Method for Path Planning Problem, by Xingrong Diao and 2 other authors
View PDFAbstract:Sampling-based path planning is a widely used method in robotics, particularly in high-dimensional state space. Among the whole process of the path planning, collision detection is the most time-consuming operation. In this paper, we propose a learning-based path planning method that aims to reduce the number of collision detection. We develop an efficient neural network model based on Graph Neural Networks (GNN) and use the environment map as input. The model outputs weights for each neighbor based on the input and current vertex information, which are used to guide the planner in avoiding obstacles. We evaluate the proposed method's efficiency through simulated random worlds and real-world experiments, respectively. The results demonstrate that the proposed method significantly reduces the number of collision detection and improves the path planning speed in high-dimensional environments.
| Subjects: | Robotics (cs.RO); Systems and Control (eess.SY) |
| Cite as: | arXiv:2309.14845 [cs.RO] |
| (orarXiv:2309.14845v2 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2309.14845 arXiv-issued DOI via DataCite |
Submission history
From: Xingrong Diao [view email][v1] Tue, 26 Sep 2023 11:20:57 UTC (2,198 KB)
[v2] Wed, 22 Nov 2023 11:59:15 UTC (2,196 KB)
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View a PDF of the paper titled Graph Neural Network Based Method for Path Planning Problem, by Xingrong Diao and 2 other authors
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