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# DPtraj # DPtraj
A double-polynomial discription for trajectory interfaced with learning-based front end. A double-polynomial discription for trajectory interfaced with learning-based front end.
The code will come in middle July.
This work is presented in the paper: Hierarchically Depicting Vehicle Trajectory with Stability in Complex Environments, published in Science Robotics. This work is presented in the paper: Hierarchically Depicting Vehicle Trajectory with Stability in Complex Environments, published in Science Robotics.
The backend trajectory optimizer improvements build upon our previous work (available at https://github.com/ZJU-FAST-Lab/Dftpav), where singularity issues were addressed. The backend trajectory optimizer improvements build upon our previous work (available at https://github.com/ZJU-FAST-Lab/Dftpav), where singularity issues were addressed.
Moreover, the approach has recently been extended and applied to more complex multi-joint robotic platforms (see https://github.com/Tracailer/Tracailer). Moreover, the approach has recently been extended and applied to more complex multi-joint robotic platforms (see https://github.com/Tracailer/Tracailer).
If you find this repository helpful, please consider citing at least one of the following papers:
```bibtex
@article{han2025hierarchically,
title={Hierarchically depicting vehicle trajectory with stability in complex environments},
author={Han, Zhichao and Tian, Mengze and Gongye, Zaitian and Xue, Donglai and Xing, Jiaxi and Wang, Qianhao and Gao, Yuman and Wang, Jingping and Xu, Chao and Gao, Fei},
journal={Science Robotics},
volume={10},
number={103},
pages={eads4551},
year={2025},
publisher={American Association for the Advancement of Science}
}
@article{han2023efficient,
title={An efficient spatial-temporal trajectory planner for autonomous vehicles in unstructured environments},
author={Han, Zhichao and Wu, Yuwei and Li, Tong and Zhang, Lu and Pei, Liuao and Xu, Long and Li, Chengyang and Ma, Changjia and Xu, Chao and Shen, Shaojie and others},
journal={IEEE Transactions on Intelligent Transportation Systems},
volume={25},
number={2},
pages={1797--1814},
year={2023},
publisher={IEEE}
}
```
The code will be divided into several modules and gradually open-sourced in different branches. Currently, you can switch to the `backend` branch to try our efficient singularity-free backend optimization. This branch includes a README to guide you through quick deployment.