Biography

I am pursuing my M.S. degree in College of Control Science and Engineering, Zhejiang University, Hangzhou, China. My main research is the control and perception of quadruped robots.

Research and Interests

• Intelligent quadruped locomotion
• Reinforcement learning

Publications

• Gang Xu, Deye Zhu, Junjie Cao, Yong Liu, and Jian Yang. Shunted Collision Avoidance for Multi-UAV Motion Planning with Posture Constraints. In 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023.
  [BibTeX] [Abstract] [DOI] [PDF]

  This paper investigates the problem of fixed-wing unmanned aerial vehicles (UAV s) motion planning with posture constraints and the problem of the more general symmetrical situations where UAVs have more than one optimal solution. In this paper, the posture constraints are formulated in the 3D Dubins method, and the symmetrical situations are overcome by a more collaborative strategy called the shunted strategy. The effectiveness of the proposed method has been validated by conducting extensive simulation experiments. Meanwhile, we compared the proposed method with the other state-of-the-art methods, and the comparison results show that the proposed method advances the previous works. Finally, the practicability of the proposed algorithm was analyzed by the statistic in computational cost. The source code of our method can be available at https://github.com/wuuya1/SCA.

  @inproceedings{xu2023sca,
  title = {Shunted Collision Avoidance for Multi-UAV Motion Planning with Posture Constraints},
  author = {Gang Xu and Deye Zhu and Junjie Cao and Yong Liu and Jian Yang},
  year = 2023,
  booktitle = {2023 IEEE International Conference on Robotics and Automation (ICRA)},
  doi = {10.1109/ICRA48891.2023.10160979},
  abstract = {This paper investigates the problem of fixed-wing unmanned aerial vehicles (UAV s) motion planning with posture constraints and the problem of the more general symmetrical situations where UAVs have more than one optimal solution. In this paper, the posture constraints are formulated in the 3D Dubins method, and the symmetrical situations are overcome by a more collaborative strategy called the shunted strategy. The effectiveness of the proposed method has been validated by conducting extensive simulation experiments. Meanwhile, we compared the proposed method with the other state-of-the-art methods, and the comparison results show that the proposed method advances the previous works. Finally, the practicability of the proposed algorithm was analyzed by the statistic in computational cost. The source code of our method can be available at https://github.com/wuuya1/SCA.}
  }

• Gang Xu, Yansong Chen, Junjie Cao, Deye Zhu, Weiwei Liu, and Yong Liu. Multivehicle Motion Planning with Posture Constraints in Real World. IEEE-ASME Transactions on Mechatronics, 27(4):2125-2133, 2022.
  [BibTeX] [Abstract] [DOI] [PDF]

  This article addresses the posture constraints problem in multivehicle motion planning for specific applications such as ground exploration tasks. Unlike most of the related work in motion planning, this article investigates more practical applications in the real world for nonholonomic unmanned ground vehicles (UGVs). In this case, a strategy of diversion is designed to optimize the smoothness of motion. Considering the problem of the posture constraints, a postured collision avoidance algorithm is proposed for the motion planning of the multiple nonholonomic UGVs. Two simulation experiments were conducted to verify the effectiveness and analyze the quantitative performance of the proposed method. Then, the practicability of the proposed algorithm was verified with an experiment in a natural environment.

  @article{xu2022mmp,
  title = {Multivehicle Motion Planning with Posture Constraints in Real World},
  author = {Gang Xu and Yansong Chen and Junjie Cao and Deye Zhu and Weiwei Liu and Yong Liu},
  year = 2022,
  journal = {IEEE-ASME Transactions on Mechatronics},
  volume = {27},
  number = {4},
  pages = {2125-2133},
  doi = {10.1109/TMECH.2022.3173130},
  abstract = {This article addresses the posture constraints problem in multivehicle motion planning for specific applications such as ground exploration tasks. Unlike most of the related work in motion planning, this article investigates more practical applications in the real world for nonholonomic unmanned ground vehicles (UGVs). In this case, a strategy of diversion is designed to optimize the smoothness of motion. Considering the problem of the posture constraints, a postured collision avoidance algorithm is proposed for the motion planning of the multiple nonholonomic UGVs. Two simulation experiments were conducted to verify the effectiveness and analyze the quantitative performance of the proposed method. Then, the practicability of the proposed algorithm was verified with an experiment in a natural environment.}
  }

• Jianxin Huang, Laijian Li, Xiangrui Zhao, Xiaolei Lang, Deye Zhu, and Yong Liu. LODM: Large-scale Online Dense Mapping for UAV. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022.
  [BibTeX] [Abstract] [DOI] [PDF]

  This paper proposes a method for online large-scale dense mapping. The UAV is within a range of 150-250 meters, combining GPS and visual odometry to estimate the scaled pose and sparse points. In order to use the depth of sparse points for depth map, we propose Sparse Confidence Cascade View-Aggregation MVSNet (SCCVA-MVSNet), which projects the depth-converged points in the sliding window on keyframes to obtain a sparse depth map. The photometric error constructs sparse confidence. The coarse depth and confidence through normalized convolution use the images of all keyframes, coarse depth, and confidence as the input of CVA-MVSNet to extract features and construct 3D cost volumes with adaptive view aggregation to balance the different stereo baselines between the keyframes. Our proposed network utilizes sparse features point information, the output of the network better maintains the consistency of the scale. Our experiments show that MVSNet using sparse feature point information outperforms image-only MVSNet, and our online reconstruction results are comparable to offline reconstruction methods. To benefit the research community, we open-source our code at https://github.com/hjxwhy/LODM.git

  @inproceedings{huang2022lls,
  title = {LODM: Large-scale Online Dense Mapping for UAV},
  author = {Jianxin Huang and Laijian Li and Xiangrui Zhao and Xiaolei Lang and Deye Zhu and Yong Liu},
  year = 2022,
  booktitle = {2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  doi = {10.1109/IROS47612.2022.9981994},
  abstract = {This paper proposes a method for online large-scale dense mapping. The UAV is within a range of 150-250 meters, combining GPS and visual odometry to estimate the scaled pose and sparse points. In order to use the depth of sparse points for depth map, we propose Sparse Confidence Cascade View-Aggregation MVSNet (SCCVA-MVSNet), which projects the depth-converged points in the sliding window on keyframes to obtain a sparse depth map. The photometric error constructs sparse confidence. The coarse depth and confidence through normalized convolution use the images of all keyframes, coarse depth, and confidence as the input of CVA-MVSNet to extract features and construct 3D cost volumes with adaptive view aggregation to balance the different stereo baselines between the keyframes. Our proposed network utilizes sparse features point information, the output of the network better maintains the consistency of the scale. Our experiments show that MVSNet using sparse feature point information outperforms image-only MVSNet, and our online reconstruction results are comparable to offline reconstruction methods. To benefit the research community, we open-source our code at https://github.com/hjxwhy/LODM.git}
  }