Biography

I am pursuing my M.S. degree in College of Control Science and Engineering, Zhejiang University, Hangzhou, China. My major research interests include motion planning and robots.

Research and Interests

• Motion Planning

Publications

• Zhen Zhang, Jiaqing Yan, Xin Kong, Guangyao Zhai, and Yong Liu. Efficient Motion Planning based on Kinodynamic Model for Quadruped Robots Following Persons in Confined Spaces. IEEE/ASME Transactions on Mechatronics, 2021.
  [BibTeX] [Abstract] [DOI] [PDF]

  Quadruped robots have superior terrain adaptability and flexible movement capabilities than traditional robots. In this paper, we innovatively apply it in person-following tasks, and propose an efficient motion planning scheme for quadruped robots to generate a flexible and effective trajectory in confined spaces. The method builds a real-time local costmap via onboard sensors, which involves both static and dynamic obstacles. And we exploit a simplified kinodynamic model and formulate the friction pyramids formed by Ground Reaction Forces (GRFs) inequality constraints to ensure the executable of the optimized trajectory. In addition, we obtain the optimal following trajectory in the costmap completely based on the robots rectangular footprint description, which ensures that it can walk through the narrow spaces avoiding collision. Finally, a receding horizon control strategy is employed to improve the robustness of motion in complex environments. The proposed motion planning framework is integrated on the quadruped robot JueYing and tested in simulation as well as real scenarios. It shows that the execution success rates in various scenes are all over 90\%.

  @article{zhang2021emp,
  title = {Efficient Motion Planning based on Kinodynamic Model for Quadruped Robots Following Persons in Confined Spaces},
  author = {Zhen Zhang and Jiaqing Yan and Xin Kong and Guangyao Zhai and Yong Liu},
  year = 2021,
  journal = {IEEE/ASME Transactions on Mechatronics},
  doi = {10.1109/TMECH.2021.3083594},
  abstract = {Quadruped robots have superior terrain adaptability and flexible movement capabilities than traditional robots. In this paper, we innovatively apply it in person-following tasks, and propose an efficient motion planning scheme for quadruped robots to generate a flexible and effective trajectory in confined spaces. The method builds a real-time local costmap via onboard sensors, which involves both static and dynamic obstacles. And we exploit a simplified kinodynamic model and formulate the friction pyramids formed by Ground Reaction Forces (GRFs) inequality constraints to ensure the executable of the optimized trajectory. In addition, we obtain the optimal following trajectory in the costmap completely based on the robots rectangular footprint description, which ensures that it can walk through the narrow spaces avoiding collision. Finally, a receding horizon control strategy is employed to improve the robustness of motion in complex environments. The proposed motion planning framework is integrated on the quadruped robot JueYing and tested in simulation as well as real scenarios. It shows that the execution success rates in various scenes are all over 90\%.}
  }

• Licheng Wen, Jiaqing Yan, Xuemeng Yang, Yong Liu, and Yong Gu. Collision-free Trajectory Planning for Autonomous Surface Vehicle. In 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), page 1098–1105, 2020.
  [BibTeX] [Abstract] [DOI] [arXiv] [PDF]

  In this paper, we propose an efficient and accurate method for autonomous surface vehicles to generate a smooth and collision-free trajectory considering its dynamics constraints. We decouple the trajectory planning problem as a front-end feasible path searching and a back-end kinodynamic trajectory optimization. Firstly, we model the type of two-thrusts under-actuated surface vessel. Then we adopt a sampling-based path searching to find an asymptotic optimal path through the obstacle-surrounding environment and extract several waypoints from it. We apply a numerical optimization method in the back-end to generate the trajectory. From the perspective of security in the field voyage, we propose the sailing corridor method to guarantee the trajectory away from obstacles. Moreover, considering limited fuel ASV carrying, we design a numerical objective function which can optimize a fuel-saving trajectory. Finally, we validate and compare the proposed method in simulation environments and the results fit our expected trajectory.

  @inproceedings{wen2020collisionfreetp,
  title = {Collision-free Trajectory Planning for Autonomous Surface Vehicle},
  author = {Licheng Wen and Jiaqing Yan and Xuemeng Yang and Yong Liu and Yong Gu},
  year = 2020,
  booktitle = {2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)},
  pages = {1098--1105},
  doi = {https://doi.org/10.1109/AIM43001.2020.9158907},
  abstract = {In this paper, we propose an efficient and accurate method for autonomous surface vehicles to generate a smooth and collision-free trajectory considering its dynamics constraints. We decouple the trajectory planning problem as a front-end feasible path searching and a back-end kinodynamic trajectory optimization. Firstly, we model the type of two-thrusts under-actuated surface vessel. Then we adopt a sampling-based path searching to find an asymptotic optimal path through the obstacle-surrounding environment and extract several waypoints from it. We apply a numerical optimization method in the back-end to generate the trajectory. From the perspective of security in the field voyage, we propose the sailing corridor method to guarantee the trajectory away from obstacles. Moreover, considering limited fuel ASV carrying, we design a numerical objective function which can optimize a fuel-saving trajectory. Finally, we validate and compare the proposed method in simulation environments and the results fit our expected trajectory.},
  arxiv = {http://arxiv.org/pdf/2005.09857}
  }