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

• Xinyang Liu, Min Lin, Shengbo Li, Gang Xu, Zhifang Wang, Huifeng Wu, and Yong Liu. FLARE: Fast Large-scale Autonomous Exploration Guided by Unknown Regions. IEEE Robotics and Automation Letters, 10:12197-12204, 2025.
  [BibTeX] [Abstract] [DOI] [PDF]

  Autonomous exploration is a critical foundation for uncrewed aerial vehicles (UAV) applications such as search and rescue. However, existing methods typically focus only on known spaces or frontiers without considering unknown regions or providing further guidance for the global path, which results in low exploration efficiency. This letter proposes FLARE, which enables Fast UAV exploration in LARge-scale and complex unknown Environments. The incremental unknown region partitioning method partitions the unexplored space into multiple unknown regions in real-time by integrating known information with the sensor perception range. Building on this, the hierarchical planner first computes a global path that encompasses all unknown regions and then generates safe and feasible local trajectories for the UAV. We evaluate the performance of FLARE through extensive simulations and real-world experiments. The results show that, compared to existing state-of-the-art algorithms, FLARE significantly improves exploration efficiency, reducing exploration time by 16.8% to 27.9% and flight distance by 15.8% to 25.5%. The source code of FLARE will be released to benefit the community.

  @article{liu2025flare,
  title = {FLARE: Fast Large-scale Autonomous Exploration Guided by Unknown Regions},
  author = {Xinyang Liu and Min Lin and Shengbo Li and Gang Xu and Zhifang Wang and Huifeng Wu and Yong Liu},
  year = 2025,
  journal = {IEEE Robotics and Automation Letters},
  volume = 10,
  pages = {12197-12204},
  doi = {10.1109/LRA.2025.3620618},
  abstract = {Autonomous exploration is a critical foundation for uncrewed aerial vehicles (UAV) applications such as search and rescue. However, existing methods typically focus only on known spaces or frontiers without considering unknown regions or providing further guidance for the global path, which results in low exploration efficiency. This letter proposes FLARE, which enables Fast UAV exploration in LARge-scale and complex unknown Environments. The incremental unknown region partitioning method partitions the unexplored space into multiple unknown regions in real-time by integrating known information with the sensor perception range. Building on this, the hierarchical planner first computes a global path that encompasses all unknown regions and then generates safe and feasible local trajectories for the UAV. We evaluate the performance of FLARE through extensive simulations and real-world experiments. The results show that, compared to existing state-of-the-art algorithms, FLARE significantly improves exploration efficiency, reducing exploration time by 16.8% to 27.9% and flight distance by 15.8% to 25.5%. The source code of FLARE will be released to benefit the community.}
  }

• Gang Xu, Yuchen Wu, Sheng Tao, Yifan Yang, Tao Liu, Tao Huang, Huifeng Wu, and Yong Liu. Efficient Multi-Robot Task and Path Planning in Large-Scale Cluttered Environments. IEEE Robotics and Automation Letters, 10:9112-9119, 2025.
  [BibTeX] [Abstract] [DOI] [PDF]

  As the potential of multi-robot systems continues to be explored and validated across various real-world applications, such as package delivery, search and rescue, and autonomous exploration, the need to improve the efficiency and quality of task and path planning has become increasingly urgent, particularly in large-scale, obstacle-rich environments. To this end, this letter investigates the problem of multi-robot task and path planning (MRTPP) in large-scale cluttered scenarios. Specifically, we first propose an obstacle-vertex search (OVS) path planner that quickly constructs the cost matrix of collision-free paths for multi-robot task planning, ensuring the rationality of task planning in obstacle-rich environments. Furthermore, we introduce an efficient auction-based method for solving the MRTPP problem by incorporating a novel memory-aware strategy, aiming to minimize the maximum travel cost among robots for task visits. The proposed method effectively improves computational efficiency while maintaining solution quality in the multi-robot task planning problem. Finally, we demonstrated the effectiveness and practicality of the proposed method through extensive benchmark comparisons.

  @article{xu2025emr,
  title = {Efficient Multi-Robot Task and Path Planning in Large-Scale Cluttered Environments},
  author = {Gang Xu and Yuchen Wu and Sheng Tao and Yifan Yang and Tao Liu and Tao Huang and Huifeng Wu and Yong Liu},
  year = 2025,
  journal = {IEEE Robotics and Automation Letters},
  volume = 10,
  pages = {9112-9119},
  doi = {10.1109/LRA.2025.3592146},
  abstract = {As the potential of multi-robot systems continues to be explored and validated across various real-world applications, such as package delivery, search and rescue, and autonomous exploration, the need to improve the efficiency and quality of task and path planning has become increasingly urgent, particularly in large-scale, obstacle-rich environments. To this end, this letter investigates the problem of multi-robot task and path planning (MRTPP) in large-scale cluttered scenarios. Specifically, we first propose an obstacle-vertex search (OVS) path planner that quickly constructs the cost matrix of collision-free paths for multi-robot task planning, ensuring the rationality of task planning in obstacle-rich environments. Furthermore, we introduce an efficient auction-based method for solving the MRTPP problem by incorporating a novel memory-aware strategy, aiming to minimize the maximum travel cost among robots for task visits. The proposed method effectively improves computational efficiency while maintaining solution quality in the multi-robot task planning problem. Finally, we demonstrated the effectiveness and practicality of the proposed method through extensive benchmark comparisons.}
  }

• Yifan Yang, Yuchen Wu, Gang Xu, Yong Liu, Zhitao Zhang, and Jian Yang. Intelligent Hybrid Decision-Making for High-Speed Autonomous Driving Scenarios. In The 25th COTA International Conference of Transportation Professionals (CICTP), 2025.
  [BibTeX]

  @inproceedings{yang2025ihd,
  title = {Intelligent Hybrid Decision-Making for High-Speed Autonomous Driving Scenarios},
  author = {Yifan Yang and Yuchen Wu and Gang Xu and Yong Liu and Zhitao Zhang and Jian Yang},
  year = 2025,
  booktitle = {The 25th COTA International Conference of Transportation Professionals (CICTP)}
  }

• Tianyang Hu, Zhen Zhang, Chengrui Zhu, Gang Xu, Yuchen Wu, Huifeng Wu, and Yong Liu. MARF: Cooperative Multi-Agent Path Finding with Reinforcement Learning and Frenet Lattice in Dynamic Environments. In 2025 IEEE International Conference on Robotics and Automation (ICRA), pages 12607-12613, 2025.
  [BibTeX] [Abstract] [DOI] [PDF]

  Multi-agent path finding (MAPF) in dynamic and complex environments is a highly challenging task. Recent research has focused on the scalability of agent numbers or the complexity of the environment. Usually, they disregard the agents’ physical constraints or use a differential-driven model. However, this approach fails to adequately capture the kinematic and dynamic constraints of real-world vehicles, particularly those equipped with Ackermann steering. This paper presents a novel algorithm named MARF that combines multi-agent reinforcement learning (MARL) with a Frenet lattice planner. The MARL foundation endows the algorithm with enhanced generalization capabilities while preserving computational efficiency. By incorporating Frenet lattice trajectories into the action space of the MARL framework, agents are capable of generating smooth and feasible trajectories that respect the kinematic and dynamic constraints. In addition, we adopt a centralized training and decentralized execution (CTDE) framework, where a network of shared value functions enables efficient cooperation among agents during decision-making. Simulation results and real-world experiments in different scenarios demonstrate that our method achieves superior performance in terms of success rate, average speed, extra distance of trajectory, and computing time.

  @inproceedings{hu2025marf,
  title = {MARF: Cooperative Multi-Agent Path Finding with Reinforcement Learning and Frenet Lattice in Dynamic Environments},
  author = {Tianyang Hu and Zhen Zhang and Chengrui Zhu and Gang Xu and Yuchen Wu and Huifeng Wu and Yong Liu},
  year = 2025,
  booktitle = {2025 IEEE International Conference on Robotics and Automation (ICRA)},
  pages = {12607-12613},
  doi = {10.1109/ICRA55743.2025.11128009},
  abstract = {Multi-agent path finding (MAPF) in dynamic and complex environments is a highly challenging task. Recent research has focused on the scalability of agent numbers or the complexity of the environment. Usually, they disregard the agents' physical constraints or use a differential-driven model. However, this approach fails to adequately capture the kinematic and dynamic constraints of real-world vehicles, particularly those equipped with Ackermann steering. This paper presents a novel algorithm named MARF that combines multi-agent reinforcement learning (MARL) with a Frenet lattice planner. The MARL foundation endows the algorithm with enhanced generalization capabilities while preserving computational efficiency. By incorporating Frenet lattice trajectories into the action space of the MARL framework, agents are capable of generating smooth and feasible trajectories that respect the kinematic and dynamic constraints. In addition, we adopt a centralized training and decentralized execution (CTDE) framework, where a network of shared value functions enables efficient cooperation among agents during decision-making. Simulation results and real-world experiments in different scenarios demonstrate that our method achieves superior performance in terms of success rate, average speed, extra distance of trajectory, and computing time.}
  }

• 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, 21:7027-7039, 2024.
  [BibTeX] [Abstract] [DOI] [PDF]

  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{xu2024dmv,
  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 = 2024,
  journal = {IEEE Transactions on Automation Science and Engineering},
  volume = 21,
  pages = {7027-7039},
  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.}
  }

• Yuchen Wu, Yifan Yang, Gang Xu, Junjie Cao, Yansong Chen, Licheng Wen, and Yong Liu. Hierarchical Search-Based Cooperative Motion Planning. In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 8055-8062, 2024.
  [BibTeX] [Abstract] [DOI] [PDF]

  Cooperative path planning, a crucial aspect of multi-agent systems research, serves a variety of sectors, including military, agriculture, and industry. Many existing algorithms, however, come with certain limitations, such as simplified kinematic models and inadequate support for multiple group scenarios. Focusing on the planning problem associated with a nonholonomic Ackermann model for Unmanned Ground Vehicles (UGV), we propose a leaderless, hierarchical Search-Based Cooperative Motion Planning (SCMP) method. The high-level utilizes a binary conflict search tree to minimize runtime, while the low-level fabricates kinematically feasible, collision-free paths that are shape-constrained. Our algorithm can adapt to scenarios featuring multiple groups with different shapes, outlier agents, and elaborate obstacles. We conduct algorithm comparisons, performance testing, simulation, and real-world testing, verifying the effectiveness and applicability of our algorithm. The implementation of our method will be open-sourced at https://github.com/WYCUniverStar/SCMP.

  @inproceedings{wu2024hsb,
  title = {Hierarchical Search-Based Cooperative Motion Planning},
  author = {Yuchen Wu and Yifan Yang and Gang Xu and Junjie Cao and Yansong Chen and Licheng Wen and Yong Liu},
  year = 2024,
  booktitle = {2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages = {8055-8062},
  doi = {10.1109/IROS58592.2024.10801442},
  abstract = {Cooperative path planning, a crucial aspect of multi-agent systems research, serves a variety of sectors, including military, agriculture, and industry. Many existing algorithms, however, come with certain limitations, such as simplified kinematic models and inadequate support for multiple group scenarios. Focusing on the planning problem associated with a nonholonomic Ackermann model for Unmanned Ground Vehicles (UGV), we propose a leaderless, hierarchical Search-Based Cooperative Motion Planning (SCMP) method. The high-level utilizes a binary conflict search tree to minimize runtime, while the low-level fabricates kinematically feasible, collision-free paths that are shape-constrained. Our algorithm can adapt to scenarios featuring multiple groups with different shapes, outlier agents, and elaborate obstacles. We conduct algorithm comparisons, performance testing, simulation, and real-world testing, verifying the effectiveness and applicability of our algorithm. The implementation of our method will be open-sourced at https://github.com/WYCUniverStar/SCMP.}
  }

• 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.}
  }