Biography

I am currently a M.S. degree candidate of the institute of Cyber Systems and Control, Department of Control Science and Engineering, Zhejiang University. I received his B.S. degree in College of Control Science and Engineering from Zhejiang University in 2019. My latest research interests include robotics and motion planning.

Research and Interests

• Motion Planning

Publications

• Weiwei Liu, Linpeng Peng, Licheng Wen, Jian Yang, and Yong Liu. Decomposing Shared Networks for Separate Cooperation with Multi-agent Reinforcement Learning. Information Sciences, 641:119085, 2023.
  [BibTeX] [Abstract] [DOI] [PDF]

  Sharing network parameters between agents is an essential and typical operation to improve the scalability of multi-agent reinforcement learning algorithms. However, agents with different tasks sharing the same network parameters are not conducive to distinguishing the agents’ skills. In addition, the importance of communication between agents undertaking the same task is much higher than that with external agents. Therefore, we propose Dual Cooperation Networks (DCN). In order to distinguish whether agents undertake the same task, all agents are grouped according to their status through the graph neural network instead of the traditional proximity. The agent communicates within the group to achieve strong cooperation. After that, the global value function is decomposed by groups to facilitate cooperation between groups. Finally, we have verified it in simulation and physical hardware, and the algorithm has achieved excellent performance.

  @article{liu2023dsn,
  title = {Decomposing Shared Networks for Separate Cooperation with Multi-agent Reinforcement Learning},
  author = {Weiwei Liu and Linpeng Peng and Licheng Wen and Jian Yang and Yong Liu},
  year = 2023,
  journal = {Information Sciences},
  volume = 641,
  pages = {119085},
  doi = {10.1016/j.ins.2023.119085},
  abstract = {Sharing network parameters between agents is an essential and typical operation to improve the scalability of multi-agent reinforcement learning algorithms. However, agents with different tasks sharing the same network parameters are not conducive to distinguishing the agents' skills. In addition, the importance of communication between agents undertaking the same task is much higher than that with external agents. Therefore, we propose Dual Cooperation Networks (DCN). In order to distinguish whether agents undertake the same task, all agents are grouped according to their status through the graph neural network instead of the traditional proximity. The agent communicates within the group to achieve strong cooperation. After that, the global value function is decomposed by groups to facilitate cooperation between groups. Finally, we have verified it in simulation and physical hardware, and the algorithm has achieved excellent performance.}
  }

• Licheng Wen, Yong Liu, and Hongliang Li. CL-MAPF: Multi-Agent Path Finding for Car-Like Robots with Kinematic and Spatiotemporal Constraints. Robotics and Autonomous Systems, 150, 2022.
  [BibTeX] [Abstract] [DOI] [PDF]

  Multi-Agent Path Finding has been widely studied in the past few years due to its broad application in the field of robotics and AI. However, previous solvers rely on several simplifying assumptions. This limits their applicability in numerous real-world domains that adopt nonholonomic car-like agents rather than holonomic ones. In this paper, we give a mathematical formalization of the Multi-Agent Path Finding for Car-Like robots (CL-MAPF) problem. We propose a novel hierarchical search-based solver called Car-Like Conflict-Based Search to address this problem. It applies a body conflict tree to address collisions considering the shapes of the agents. We introduce a new algorithm called Spatiotemporal Hybrid-State A* as the single-agent planner to generate agents’ paths satisfying both kinematic and spatiotemporal constraints. We also present a sequential planning version of our method, sacrificing a small amount of solution quality to achieve a significant reduction in runtime. We compare our method with two baseline algorithms on a dedicated benchmark and validate it in real-world scenarios. The experiment results show that the planning success rate of both baseline algorithms is below 50% for all six scenarios, while our algorithm maintains that of over 98%. It also gives clear evidence that our algorithm scales well to 100 agents in 300 m x 300 m scenario and is able to produce solutions that can be directly applied to Ackermann-steering robots in the real world. The benchmark and source code are released in https://github.com/APRIL-ZJU/CL-CBS. The video of the experiments can be found on YouTube.(C) 2021 Elsevier B.V. All rights reserved.

  @article{wen2022clm,
  title = {CL-MAPF: Multi-Agent Path Finding for Car-Like Robots with Kinematic and Spatiotemporal Constraints},
  author = {Licheng Wen and Yong Liu and Hongliang Li},
  year = 2022,
  journal = {Robotics and Autonomous Systems},
  volume = 150,
  doi = {10.1016/j.robot.2021.103997},
  abstract = {Multi-Agent Path Finding has been widely studied in the past few years due to its broad application in the field of robotics and AI. However, previous solvers rely on several simplifying assumptions. This limits their applicability in numerous real-world domains that adopt nonholonomic car-like agents rather than holonomic ones. In this paper, we give a mathematical formalization of the Multi-Agent Path Finding for Car-Like robots (CL-MAPF) problem. We propose a novel hierarchical search-based solver called Car-Like Conflict-Based Search to address this problem. It applies a body conflict tree to address collisions considering the shapes of the agents. We introduce a new algorithm called Spatiotemporal Hybrid-State A* as the single-agent planner to generate agents' paths satisfying both kinematic and spatiotemporal constraints. We also present a sequential planning version of our method, sacrificing a small amount of solution quality to achieve a significant reduction in runtime. We compare our method with two baseline algorithms on a dedicated benchmark and validate it in real-world scenarios. The experiment results show that the planning success rate of both baseline algorithms is below 50% for all six scenarios, while our algorithm maintains that of over 98%. It also gives clear evidence that our algorithm scales well to 100 agents in 300 m x 300 m scenario and is able to produce solutions that can be directly applied to Ackermann-steering robots in the real world. The benchmark and source code are released in https://github.com/APRIL-ZJU/CL-CBS. The video of the experiments can be found on YouTube.(C) 2021 Elsevier B.V. All rights reserved.}
  }

• 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}
  }