工作单位：海洋工程与技术学院
专业资格：副教授、硕士生导师
电子邮箱：liutao55@mail.sysu.edu.cn
研究方向：水声信息感知与目标探测技术
招生专业：船舶与海洋工程、电子信息。课题组科研经费充足、科研设施完善，欢迎船舶与海洋工程、自动化、机器人工程、机械电子等相关专业背景的同学保送及报考！
个人详细信息:
刘涛，男，中共党员，博士，副教授，硕士生导师。目前主要从事机器人总体设计与智能控制技术、水声信息感知与目标探测技术研究，包括机器人总体设计与系统集成、水下航行器智能感知规划控制、机电一体化、水下感知与目标探测技术等。近年主持国防重点实验室项目、国家重点研发计划项目子课题、广东省面上等多个项目的研究工作；近年来第一/通讯作者在Mechanism and Machine Theory、ISA Transactions、Journal of Field Robotics、Ocean Engineering等高水平期刊会议发表论文20余篇，长期担任Expert Systems with Applications、Engineering Applications of Artificial Intelligence、Ocean Engineering、Nonlinear Dynamics等高水平期刊审稿人。指导学生多次参加学科竞赛活动并获奖，指导学生获国家奖学金、优秀毕业论文等荣誉。课题组科研经费充足、科研设施完善，欢迎对水下机器人创新设计、智能规划控制、强化学习等研究方向感兴趣的同学交流、报考。
教育经历：
2009.09-2013.06，西北工业大学航海学院，机械设计制造及其自动化，工学学士
2013.09-2016.03，西北工业大学航海学院，船舶与海洋结构物设计制造，工学硕士
2016.03-2021.12，西北工业大学航海学院，兵器科学与技术，工学博士
工作经历：
2021.12-2025.03，中山大学海洋工程与技术学院，助理教授。
2025.04-至今，中山大学海洋工程与技术学院，副教授；
科研项目：
[1] 2025.11-2027.11，水声技术全国重点实验室基金项目，基于UUV平台的某关键技术研究，主持
[2] 2024.09-2026.12，南方海洋实验室科技计划项目，基于UUV拖曳的深海水声目标探测技术，主持
[3] 2024.10-2026.10，声呐技术国防重点实验室基金项目，基于AUV拖曳阵的深海直达声区目标探测方法研究，主持；
[4] 2023.12-2026.11，国家重点研发计划子课题，深拖安全预警保障软件开发，主持；
[5] 2025.01-2027.12，广东省自然科学基金面上项目，海水磨蚀作用下水下航行器动力学建模与智能控制策略研究，主持；
[6] 2023.3-2023.08，国防横向项目，某航空组网软件开发，主持；
[7] 2024.03-2025.03，青年教师培育项目，面向水下航行器高机动航行的离-在线混合强化学习研究，主持。
近5年代表性论文：
[1] Liu T*, Huang J, Zhao J. Safe Diffusion Q-Learning: A Hybrid Method Combining Diffusion Model and Constrained Reinforcement Learning for AUV Navigation[J]. Ocean Engineering, 2026, 349, 124215.
[2] Zhao J, Liu T*, Huang J. DIA-MPPI: A Diffusion-Inspired Control Framework for Efficient Path Optimization in Autonomous Underwater Vehicle Fleets[J]. Ocean Engineering, 2026， 343, 123558.
[3] Liu T*, Huang J, Zhao J. Adaptive Multi-AUV Navigation via Hybrid Offline-Online Reinforcement Learning with ORCA Integration[J]. Ocean Engineering, 2025, 341: 122789.
[4] Zhao J, Liu T*, Huang J. Enhanced Obstacle Avoidance for Autonomous Underwater Vehicles via Path Integral Control Based on Guiding Vector Field[J]. ISA Transactions, 2025; 1–16.
[5] Zhao J, Liu T*, Huang J. Reinforcement Learning Based Model Predictive Path Integral Control for Obstacles Avoidance of Autonomous Underwater Vehicles[J]. Journal of Field Robotics, 2025; 1–16.
[6] Liu T*, Zhao J, Huang J, et al. A hybrid RVO-MPPI approach for efficient collision avoidance for multiple autonomous underwater vehicles[J]. Ocean Engineering, 2024, 312: 119205.
[7] Liu T*, Zhao J, Huang J, et al. Research on model predictive control of autonomous underwater vehicle based on physics informed neural network modeling[J]. Ocean Engineering, 2024, 304: 117844.
[8] Liu T*, Zhao J, Huang J. Kinematic analysis and advanced control of a vectored thruster based on 3RRUR parallel manipulator for micro-size AUVs [J]. Robotica, 2024: 1-17.
[9] Liu T*, Zhao J, Hu Y, et al. Trajectory tracking control of vectored thruster autonomous underwater vehicles based on deep reinforcement learning[J]. Ships and Offshore Structures, 2024: 1-14.
[10] Liu T*, Zhao J, Huang J. A Gaussian-Process-Based Model Predictive Control Approach for Trajectory Tracking and Obstacle Avoidance in Autonomous Underwater Vehicles[J]. Journal of Marine Science and Engineering, 2024, 12(4): 676.
[11] Liu T, Huang J, Zhao J. Research on obstacle avoidance of underactuated autonomous underwater vehicle based on offline reinforcement learning[J]. Robotica, 2024: 1-25.
[12] Zhao J, Liu T*, Huang J. Hybrid offline-online reinforcement learning for obstacle avoidance in autonomous underwater vehicles[J]. Ships and Offshore Structures, 2024: 1-16.
[13] Huang J, Liu T*, Zhao J. Diffusion-enhanced Reinforcement Learning for Autonomous Underwater Vehicle Navigation[C]//2025 12th International Forum on Electrical Engineering and Automation (IFEEA). IEEE, 2025: 159-164.
[14] Liu T*, Huang J, Zhao J. Autonomous Underwater Vehicle Trajectory Tracking Based on Physics Informed Neural Network Modeling[C]//2024 3rd International Conference on Innovations and Development of Information Technologies and Robotics (IDITR). IEEE, 2024: 7-12.
[15] Liu T*, Huang J, Zhao J. Research on Control Problems of vectored thruster AUVs with deep reinforcement learning[C]//2024 3rd International Conference on Innovations and Development of Information Technologies and Robotics (IDITR). IEEE, 2024: 1-6.
[16] Liu T*, Zhao J. Research on 3D Obstacle Avoidance of Autonomous Underwater Vehicle Based on Deep Reinforcement Learning[C]//International Conference on Autonomous Unmanned Systems. Singapore: Springer Nature Singapore, 2023: 397-404.
[17] Liu T*, Wang Z, Li F, et al. Research on a vectored thruster based on 3RRUR for micro AUVs[C]//OCEANS 2022, Hampton Roads. IEEE, 2022: 1-7.
近5年专利及软件著作权：
[1] 一种融合条件风险价值与模型预测路径积分的导航方法，国家发明专利，刘涛；黄俊浩；赵锦涛；李整林；李承帮；方子哲，202511600047.6
[2] 一种融合改进人工势场法和模型预测路径积分的导航方法，国家发明专利，刘涛；赵锦涛；黄俊浩；李整林；李承帮；方子哲，202511600045.7
[3] 基于安全扩散强化学习的水下航行器自主导航方法和设备，国家发明专利，刘涛；黄俊浩；赵锦涛；刘东晔；李整林；李承帮；方子哲，202610161352.8
[4] 一种水下航行器编队控制方法和相关设备，刘涛；赵锦涛；黄俊浩；刘东晔；李整林；李承帮；方子哲，国家发明专利，202610161360.2
[5] 深拖水下作业安全预警系统，刘涛；黄俊浩；赵锦涛，2025R11S2053742
