Biography

I am pursuing my Ph.D. degree in Control Engineering, Zhejiang University, Hangzhou, China. My major research interests are task assignment, path planning, and motion planning in swarm robotics.

Research and Interests

• Swarm Robotics
• Aerial and Mobile Robots
• Path and Motion Planning

Publications

• Gang Xu, Xiao Kang, Helei Yang, Yuchen Wu, Weiwei Liu, Junjie Cao, and Yong Liu. Distributed Multi-Vehicle Task Assignment and Motion Planning in Dense Environments. IEEE Transactions on Automation Science and Engineering, 2023.
  [BibTeX] [Abstract] [DOI]

  This article investigates the multi-vehicle task assignment and motion planning (MVTAMP) problem. In a dense environment, a fleet of non-holonomic vehicles is appointed to visit a series of target positions and then move to a specific ending area for real-world applications such as clearing threat targets, aid rescue, and package delivery. We presented a novel hierarchical method to simultaneously address the multiple vehicles’ task assignment and motion planning problem. Unlike most related work, our method considers the MVTAMP problem applied to non-holonomic vehicles in large-scale scenarios. At the high level, we proposed a novel distributed algorithm to address task assignment, which produces a closer to the optimal task assignment scheme by reducing the intersection paths between vehicles and tasks or between tasks and tasks. At the low level, we proposed a novel distributed motion planning algorithm that addresses the vehicle deadlocks in local planning and then quickly generates a feasible new velocity for the non-holonomic vehicle in dense environments, guaranteeing that each vehicle efficiently visits its assigned target positions. Extensive simulation experiments in large-scale scenarios for non-holonomic vehicles and two real-world experiments demonstrate the effectiveness and advantages of our method in practical applications. The source code of our method can be available at https://github.com/wuuya1/LRGO. Note to Practitioners-The motivation for this article stems from the need to solve the multi-vehicle task assignment and motion planning (MVTAMP) problem for non-holonomic vehicles in dense environments. Many real-world applications exist, such as clearing threat targets, aid rescue, and package delivery. However, when vehicles need to continuously visit a series of assigned targets, motion planning for non-holonomic vehicles becomes more difficult because it is more likely to occur sharp turns between adjacent target path nodes. In this case, a better task allocation scheme can often lead to more efficient target visits and save all vehicles’ total traveling distance. To bridge this, we proposed a hierarchical method for solving the MVTAMP problem in large-scale complex scenarios. The numerous large-scale simulations and two real-world experiments show the effectiveness of the proposed method. Our future work will focus on the integrated task assignment and motion planning problem for non-holonomic vehicles in highly dynamic scenarios.

  @article{xu2023dmv,
  title = {Distributed Multi-Vehicle Task Assignment and Motion Planning in Dense Environments},
  author = {Gang Xu and Xiao Kang and Helei Yang and Yuchen Wu and Weiwei Liu and Junjie Cao and Yong Liu},
  year = 2023,
  journal = {IEEE Transactions on Automation Science and Engineering},
  doi = {10.1109/TASE.2023.3336076},
  abstract = {This article investigates the multi-vehicle task assignment and motion planning (MVTAMP) problem. In a dense environment, a fleet of non-holonomic vehicles is appointed to visit a series of target positions and then move to a specific ending area for real-world applications such as clearing threat targets, aid rescue, and package delivery. We presented a novel hierarchical method to simultaneously address the multiple vehicles' task assignment and motion planning problem. Unlike most related work, our method considers the MVTAMP problem applied to non-holonomic vehicles in large-scale scenarios. At the high level, we proposed a novel distributed algorithm to address task assignment, which produces a closer to the optimal task assignment scheme by reducing the intersection paths between vehicles and tasks or between tasks and tasks. At the low level, we proposed a novel distributed motion planning algorithm that addresses the vehicle deadlocks in local planning and then quickly generates a feasible new velocity for the non-holonomic vehicle in dense environments, guaranteeing that each vehicle efficiently visits its assigned target positions. Extensive simulation experiments in large-scale scenarios for non-holonomic vehicles and two real-world experiments demonstrate the effectiveness and advantages of our method in practical applications. The source code of our method can be available at https://github.com/wuuya1/LRGO. Note to Practitioners-The motivation for this article stems from the need to solve the multi-vehicle task assignment and motion planning (MVTAMP) problem for non-holonomic vehicles in dense environments. Many real-world applications exist, such as clearing threat targets, aid rescue, and package delivery. However, when vehicles need to continuously visit a series of assigned targets, motion planning for non-holonomic vehicles becomes more difficult because it is more likely to occur sharp turns between adjacent target path nodes. In this case, a better task allocation scheme can often lead to more efficient target visits and save all vehicles' total traveling distance. To bridge this, we proposed a hierarchical method for solving the MVTAMP problem in large-scale complex scenarios. The numerous large-scale simulations and two real-world experiments show the effectiveness of the proposed method. Our future work will focus on the integrated task assignment and motion planning problem for non-holonomic vehicles in highly dynamic scenarios.}
  }

• 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), pages 3671-3678, 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)},
  pages = {3671-3678},
  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.}
  }