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Xuesu Xiao, Joydeep Biswas, and Peter
Stone. Learning Inverse Kinodynamics for Accurate High-Speed Off-Road Navigation on Unstructured Terrain. In Opportunities
and Challenges with Autonomous Racing Workshop at the 2021 IEEE International Conference on Robotics and Automation (ICRA
2021), June 2021.
Video
This paper presents a learning-based approach to consider the effect of unobservable world states in kinodynamic motion planning in order to enable accurate high-speed off- road navigation on unstructured terrain. Existing kinodynamic motion planners either operate in structured and homogeneous environments and thus do not need to explicitly account for terrain-vehicle interaction, or assume a set of discrete terrain classes. However, when operating on unstructured terrain, espe- cially at high speeds, even small variations in the environment will be magnified and cause inaccurate plan execution. In this paper, to capture the complex kinodynamic model and mathematically unknown world state, we learn a kinodynamic planner in a data-driven manner with onboard inertial obser- vations. Our approach is tested on a physical robot in different indoor and outdoor environments, enables fast and accurate off-road navigation, and outperforms environment-independent alternatives, demonstrating 52.4 percent to 86.9 percent improvement in terms of plan execution success rate while traveling at high speeds.
@inproceedings{icra21ws-xiao,
title={Learning Inverse Kinodynamics for Accurate High-Speed Off-Road Navigation on Unstructured Terrain},
author={Xuesu Xiao and Joydeep Biswas and Peter Stone},
booktitle={Opportunities and Challenges with Autonomous Racing Workshop at the 2021 IEEE International Conference on Robotics and Automation (ICRA 2021)},
abstract={This paper presents a learning-based approach to consider the effect of unobservable world states in kinodynamic motion planning in order to enable accurate high-speed off- road navigation on unstructured terrain. Existing kinodynamic motion planners either operate in structured and homogeneous environments and thus do not need to explicitly account for terrain-vehicle interaction, or assume a set of discrete terrain classes. However, when operating on unstructured terrain, espe- cially at high speeds, even small variations in the environment will be magnified and cause inaccurate plan execution. In this paper, to capture the complex kinodynamic model and mathematically unknown world state, we learn a kinodynamic planner in a data-driven manner with onboard inertial obser- vations. Our approach is tested on a physical robot in different indoor and outdoor environments, enables fast and accurate off-road navigation, and outperforms environment-independent alternatives, demonstrating 52.4 percent to 86.9 percent improvement in terms of plan execution success rate while traveling at high speeds.
},
location={Xi'an, China},
year={2021},
month={June},
wwwnote={<a href="https://www.youtube.com/watch?v=KwxP3apb38A">Video</a>}
}
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