Biography

I am pursuing my master degree in College of Control Engineering, Zhejiang University, Hangzhou, China.My major research interest is Machine learning, Sequential decision-making and Robotics.

Research and Interests

• Machine learning,
• Sequential decision making
• Robotics

Publications

• Yansong Chen, Yuchen Wu, Helei Yang, Junjie Cao, Qinqin Wang, and Yong Liu. A Distributed Pipeline for Collaborative Pursuit in the Target Guarding Problem. IEEE Robotics and Automation Letters (RA-L), 9:2064-2071, 2024.
  [BibTeX] [Abstract] [DOI] [PDF]

  The target guarding problem (TGP) is a classical combat game where pursuers aim to capture evaders to protect a territory from intrusion. This paper proposes a distributed pipeline for multi-pursuer multi-evader TGP with the capability to accommodate varying numbers of evaders and criteria for successful pursuit. The pipeline integrates a cooperative encirclement-oriented distributed model predictive control (CEO-DMPC) method with a collaborative grouping strategy for trajectory planning of pursuers. This integration achieves cooperation and collision avoidance during the capture process across various scenarios. Besides, the objective function of CEO-DMPC employs sequences of predicted states instead of only a terminal state. Evaders are guided by the artificial potential field (APF) policy to reach their goals without being captured. Simulations with different parameters are conducted to validate the whole pipeline and the experiment results are illustrated and analyzed.

  @article{chen2024adp,
  title = {A Distributed Pipeline for Collaborative Pursuit in the Target Guarding Problem},
  author = {Yansong Chen and Yuchen Wu and Helei Yang and Junjie Cao and Qinqin Wang and Yong Liu},
  year = 2024,
  journal = {IEEE Robotics and Automation Letters (RA-L)},
  volume = 9,
  pages = {2064-2071},
  doi = {10.1109/LRA.2024.3349977},
  abstract = {The target guarding problem (TGP) is a classical combat game where pursuers aim to capture evaders to protect a territory from intrusion. This paper proposes a distributed pipeline for multi-pursuer multi-evader TGP with the capability to accommodate varying numbers of evaders and criteria for successful pursuit. The pipeline integrates a cooperative encirclement-oriented distributed model predictive control (CEO-DMPC) method with a collaborative grouping strategy for trajectory planning of pursuers. This integration achieves cooperation and collision avoidance during the capture process across various scenarios. Besides, the objective function of CEO-DMPC employs sequences of predicted states instead of only a terminal state. Evaders are guided by the artificial potential field (APF) policy to reach their goals without being captured. Simulations with different parameters are conducted to validate the whole pipeline and the experiment results are illustrated and analyzed.}
  }

• 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.}
  }

• Helei Yang, Peng Ge, Junjie Cao, Yifan Yang, and Yong Liu. Large Scale Pursuit-Evasion Under Collision Avoidance Using Deep Reinforcement Learning. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 2232-2239, 2023.
  [BibTeX] [Abstract] [DOI] [PDF]

  This paper examines a pursuit-evasion game (PEG) involving multiple pursuers and evaders. The decentralized pursuers aim to collaborate to capture the faster evaders while avoiding collisions. The policies of all agents are learning-based and are subjected to kinematic constraints that are specific to unicycles. To address the challenge of high dimensionality encountered in large-scale scenarios, we propose a state processing method named Mix-Attention, which is based on Self-Attention. This method effectively mitigates the curse of dimensionality. The simulation results provided in this study demonstrate that the combination of Mix-Attention and Independent Proximal Policy Optimization (IPPO) surpasses alternative approaches when solving the multi-pursuer multi-evader PEG, particularly as the number of entities increases. Moreover, the trained policies showcase their ability to adapt to scenarios involving varying numbers of agents and obstacles without requiring retraining. This adaptability showcases their transferability and robustness. Finally, our proposed approach has been validated through physical experiments conducted with six robots.

  @inproceedings{yang2023lsp,
  title = {Large Scale Pursuit-Evasion Under Collision Avoidance Using Deep Reinforcement Learning},
  author = {Helei Yang and Peng Ge and Junjie Cao and Yifan Yang and Yong Liu},
  year = 2023,
  booktitle = {2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages = {2232-2239},
  doi = {10.1109/IROS55552.2023.10341975},
  abstract = {This paper examines a pursuit-evasion game (PEG) involving multiple pursuers and evaders. The decentralized pursuers aim to collaborate to capture the faster evaders while avoiding collisions. The policies of all agents are learning-based and are subjected to kinematic constraints that are specific to unicycles. To address the challenge of high dimensionality encountered in large-scale scenarios, we propose a state processing method named Mix-Attention, which is based on Self-Attention. This method effectively mitigates the curse of dimensionality. The simulation results provided in this study demonstrate that the combination of Mix-Attention and Independent Proximal Policy Optimization (IPPO) surpasses alternative approaches when solving the multi-pursuer multi-evader PEG, particularly as the number of entities increases. Moreover, the trained policies showcase their ability to adapt to scenarios involving varying numbers of agents and obstacles without requiring retraining. This adaptability showcases their transferability and robustness. Finally, our proposed approach has been validated through physical experiments conducted with six robots.}
  }