Biography

I am pursuing my Ph.D. degree in Control Engineering, Zhejiang University, Hangzhou, China. My major research interest is Motion Planning and Machine Learning.

Research and Interests

• Motion Planning
• Machine Learning

Publications

• Tao Huang, Zhe Chen, Wang Gao, Zhenfeng Xue, and Yong Liu. A USV-UAV Cooperative Trajectory Planning Algorithm with Hull Dynamic Constraints. Sensors, 23:1845, 2023.
  [BibTeX] [Abstract] [DOI] [PDF]

  Efficient trajectory generation in complex dynamic environments remains an open problem in the operation of an unmanned surface vehicle (USV). The perception of a USV is usually interfered by the swing of the hull and the ambient weather, making it challenging to plan optimal USV trajectories. In this paper, a cooperative trajectory planning algorithm for a coupled USV-UAV system is proposed to ensure that a USV can execute a safe and smooth path as it autonomously advances through multi-obstacle maps. Specifically, the unmanned aerial vehicle (UAV) plays the role of a flight sensor, providing real-time global map and obstacle information with a lightweight semantic segmentation network and 3D projection transformation. An initial obstacle avoidance trajectory is generated by a graph-based search method. Concerning the unique under-actuated kinematic characteristics of the USV, a numerical optimization method based on hull dynamic constraints is introduced to make the trajectory easier to be tracked for motion control. Finally, a motion control method based on NMPC with the lowest energy consumption constraint during execution is proposed. Experimental results verify the effectiveness of the whole system, and the generated trajectory is locally optimal for USV with considerable tracking accuracy.

  @article{huang2023usv,
  title = {A USV-UAV Cooperative Trajectory Planning Algorithm with Hull Dynamic Constraints},
  author = {Tao Huang and Zhe Chen and Wang Gao and Zhenfeng Xue and Yong Liu},
  year = 2023,
  journal = {Sensors},
  volume = 23,
  pages = {1845},
  doi = {10.3390/s23041845},
  abstract = {Efficient trajectory generation in complex dynamic environments remains an open problem in the operation of an unmanned surface vehicle (USV). The perception of a USV is usually interfered by the swing of the hull and the ambient weather, making it challenging to plan optimal USV trajectories. In this paper, a cooperative trajectory planning algorithm for a coupled USV-UAV system is proposed to ensure that a USV can execute a safe and smooth path as it autonomously advances through multi-obstacle maps. Specifically, the unmanned aerial vehicle (UAV) plays the role of a flight sensor, providing real-time global map and obstacle information with a lightweight semantic segmentation network and 3D projection transformation. An initial obstacle avoidance trajectory is generated by a graph-based search method. Concerning the unique under-actuated kinematic characteristics of the USV, a numerical optimization method based on hull dynamic constraints is introduced to make the trajectory easier to be tracked for motion control. Finally, a motion control method based on NMPC with the lowest energy consumption constraint during execution is proposed. Experimental results verify the effectiveness of the whole system, and the generated trajectory is locally optimal for USV with considerable tracking accuracy.}
  }

• Tao Huang, Zhenfeng Xue, Zhe Chen, and Yong Liu. Efficient Trajectory Planning and Control for USV with Vessel Dynamics and Differential Flatness. In 2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pages 1273-1280, 2023.
  [BibTeX] [Abstract] [DOI] [PDF]

  Unmanned surface vessels (USVs) are widely used in ocean exploration and environmental protection. To ensure that USV can successfully perform its mission, trajectory planning and motion tracking are the two most critical technologies. This paper proposes a novel trajectory generation and tracking method for USV based on optimization theory. Specifically, the USV dynamic model is combined with differential flatness, so that the trajectory can be generated by dynamic RRT* in a linear invariant system expression form under the objective of optimal boundary value. We adjust the trajectory through local optimization to reduce the number of samples and improve efficiency. The dynamic constraints are considered in the optimization process so that the generated trajectory conforms to the kinematic characteristics of the under-actuated hull, making tracking easier. Finally, motion tracking is added with model predictive control under a sequential quadratic programming problem. Simulated results show that the planned trajectory is more consistent with the kinematic characteristics of USV, and the tracking accuracy remains at a higher level.

  @inproceedings{huang2023etp,
  title = {Efficient Trajectory Planning and Control for USV with Vessel Dynamics and Differential Flatness},
  author = {Tao Huang and Zhenfeng Xue and Zhe Chen and Yong Liu},
  year = 2023,
  booktitle = {2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)},
  pages = {1273-1280},
  doi = {10.1109/AIM46323.2023.10196111},
  abstract = {Unmanned surface vessels (USVs) are widely used in ocean exploration and environmental protection. To ensure that USV can successfully perform its mission, trajectory planning and motion tracking are the two most critical technologies. This paper proposes a novel trajectory generation and tracking method for USV based on optimization theory. Specifically, the USV dynamic model is combined with differential flatness, so that the trajectory can be generated by dynamic RRT* in a linear invariant system expression form under the objective of optimal boundary value. We adjust the trajectory through local optimization to reduce the number of samples and improve efficiency. The dynamic constraints are considered in the optimization process so that the generated trajectory conforms to the kinematic characteristics of the under-actuated hull, making tracking easier. Finally, motion tracking is added with model predictive control under a sequential quadratic programming problem. Simulated results show that the planned trajectory is more consistent with the kinematic characteristics of USV, and the tracking accuracy remains at a higher level.}
  }

• Zhe Chen, Tao Huang, Zhenfeng Xue, Zongzhi Zhu, Jinhong Xu, and Yong Liu. A Novel Unmanned Surface Vehicle with 2D-3D Fused Perception and Obstacle Avoidance Module. In 2021 IEEE International Conference on Robotics and Biomimetics (ROBIO), pages 1804-1809, 2021.
  [BibTeX] [Abstract] [DOI] [PDF]

  Unmanned surface vehicles (USVs) are important intelligent equipment that can accomplish various tasks on open area marine. During operation, environmental perception and obstacle avoidance is of vital significance to its autonomy. In this paper, we propose a novel USV equipped with fused perception and obstacle avoidance module that contains robust perception, localization and effective obstacle avoidance strategy. The new module is named Three-Dimensional Perception Module (PMTD), which utilizes camera and LiDAR to integrate multi-dimensional environmental information. It is able to detect, identify and track target objects in the process of autonomous travel. The localization precision achieves a centimeter-level with GPS and IMU devices. Meanwhile, the obstacle avoidance strategy allows the USV to efficiently keep away from static and dynamic floating objects in water areas. Through real-world experiments, we show that with the help of the proposed module, the USV can complete stable and autonomous operation and obstacles avoidance path planning even without any manual intervention. This indicates the strong ability of the module in autonomous driving for USVs.

  @inproceedings{chen2021anu,
  title = {A Novel Unmanned Surface Vehicle with 2D-3D Fused Perception and Obstacle Avoidance Module},
  author = {Zhe Chen and Tao Huang and Zhenfeng Xue and Zongzhi Zhu and Jinhong Xu and Yong Liu},
  year = 2021,
  booktitle = {2021 IEEE International Conference on Robotics and Biomimetics (ROBIO)},
  pages = {1804-1809},
  doi = {https://doi.org/10.1109/ROBIO54168.2021.9739449},
  abstract = {Unmanned surface vehicles (USVs) are important intelligent equipment that can accomplish various tasks on open area marine. During operation, environmental perception and obstacle avoidance is of vital significance to its autonomy. In this paper, we propose a novel USV equipped with fused perception and obstacle avoidance module that contains robust perception, localization and effective obstacle avoidance strategy. The new module is named Three-Dimensional Perception Module (PMTD), which utilizes camera and LiDAR to integrate multi-dimensional environmental information. It is able to detect, identify and track target objects in the process of autonomous travel. The localization precision achieves a centimeter-level with GPS and IMU devices. Meanwhile, the obstacle avoidance strategy allows the USV to efficiently keep away from static and dynamic floating objects in water areas. Through real-world experiments, we show that with the help of the proposed module, the USV can complete stable and autonomous operation and obstacles avoidance path planning even without any manual intervention. This indicates the strong ability of the module in autonomous driving for USVs.}
  }