The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Motion Planning Networks
Open accessAbstract
Fast and efficient motion planning algorithms are crucial for many state-of-the-art robotics applications such as self-driving cars. Existing motion planning methods become ineffective as their computational complexity increases exponentially with the dimensionality of the motion planning problem. To address this issue, we present Motion Planning Networks (MPNet), a neural network-based novel planning algorithm. The proposed method encodes the given workspaces directly from a point cloud measurement and generates the end-to-end collision-free paths for the given start and goal configurations. We evaluate MPNet on various 2D and 3D environments including the planning of a 7 DOF Baxter robot manipulator. The results show that MPNet is not only consistently computationally efficient in all environments but also generalizes to completely unseen environments. The results also show that the computation time of MPNet consistently remains less than 1 second in all presented experiments, which is significantly lower than existing state-of-the-art motion planning algorithms.
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Cites
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Cited by
Deep learning in robotics: survey on model structures and training strategies
Karoly, A. I., Galambos, P., Kuti, J., & Rudas, I. J. (2021) · IEEE Transactions on Systems, Man, and Cybernetics: Systems · doi:10.1109/tsmc.2020.3018325 · Cited by 146
Morales, E. F., Murrieta-Cid, R., Becerra, I., & Esquivel-Basaldua, M. A. (2021) · Intelligent Service Robotics · doi:10.1007/s11370-021-00398-z · Cited by 101
Towards learning-based planning: The nuPlan benchmark for real-world autonomous driving
Karnchanachari, N., Geromichalos, D., Tan, K. S., Li, N., Eriksen, C., Yaghoubi, S., Mehdipour, N., Bernasconi, G., Fong, W. K., Guo, Y., et al. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra57147.2024.10610077 · Cited by 46
“…the exploration for sampling-based path-planners [55], using a learning model to improve the efficiency of sampling-based motion-planners in high dimensional setting [56], applying optimizer to actively rectifies the learning model’s plan to satisfy the safety and comfort requirements [57], [40].”
MethodReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation
Xia, F., Li, C., Martín-Martín, R., Litany, O., Toshev, A., & Savarese, S. (2020) · arXiv · doi:10.48550/arxiv.2008.07792 · Cited by 33
Metadata
- Authors
- Qureshi, A. H., Simeonov, A., Bency, M. J., & Yip, M. C.
- Venue
- 2019 International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2019-05
- Pages
- 2118-2124
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2808278079
- Cited by
- 253
- References
- 29
- FWCI
- 11.05
- Type
- conference paper
- Language
- en
- Open access
- green
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- Qureshi, A. H., Simeonov, A., Bency, M. J., & Yip, M. C. (2019). Motion Planning Networks. 2019 International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/icra.2019.8793889
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