Computer Science > Robotics
arXiv:2310.02648 (cs)
[Submitted on 4 Oct 2023]
Title:Long-Term Dynamic Window Approach for Kinodynamic Local Planning in Static and Crowd Environments
View a PDF of the paper titled Long-Term Dynamic Window Approach for Kinodynamic Local Planning in Static and Crowd Environments, by Zhiqiang Jian and 5 other authors
View PDFAbstract:Local planning for a differential wheeled robot is designed to generate kinodynamic feasible actions that guide the robot to a goal position along the navigation path while avoiding obstacles. Reactive, predictive, and learning-based methods are widely used in local planning. However, few of them can fit static and crowd environments while satisfying kinodynamic constraints simultaneously. To solve this problem, we propose a novel local planning method. The method applies a long-term dynamic window approach to generate an initial trajectory and then optimizes it with graph optimization. The method can plan actions under the robot's kinodynamic constraints in real time while allowing the generated actions to be safer and more jitterless. Experimental results show that the proposed method adapts well to crowd and static environments and outperforms most SOTA approaches.
| Comments: | 9 pages, 7 figures |
| Subjects: | Robotics (cs.RO) |
| Cite as: | arXiv:2310.02648 [cs.RO] |
| (orarXiv:2310.02648v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2310.02648 arXiv-issued DOI via DataCite | |
| Journal reference: | 2023 IEEE RA-L |
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View a PDF of the paper titled Long-Term Dynamic Window Approach for Kinodynamic Local Planning in Static and Crowd Environments, by Zhiqiang Jian and 5 other authors
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