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Stepwise Cooperative Trajectory Planning for Multiple BUVs Based on Temporal-Spatial Bezier Curves 期刊论文
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2023, 卷号: 72, 页码: 14
作者:  Wang, Rui;  Jiang, Tianyu;  Bai, Ge;  Wang, Yu;  Wang, Shuo;  Tan, Min
收藏  |  浏览/下载：99/0  |  提交时间：2023/11/17
Trajectory planning  Trajectory  Planning  Vehicle dynamics  Robots  Underwater vehicles  Real-time systems  Bezier curve  biomimetic underwater vehicle (BUV)  cooperative trajectory planning  dynamic obstacle avoidance  
A Collision-Free Planning and Control Framework for a Biomimetic Underwater Vehicle in Dynamic Environments 期刊论文
IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2023, 卷号: 28, 期号: 3, 页码: 1415 - 1424
作者:  Lv, Jiaqi;  Wang, Yu;  Wang, Shuo;  Bai, Xuejian;  Wang, Rui;  Tan, Min
Adobe PDF(3810Kb)  |  收藏  |  浏览/下载：340/69  |  提交时间：2023/02/22
Arctangent nonsingularity terminal sliding mode (ANTSM) control  biomimetic underwater vehicle (BUV)  collision-free in dynamic environments  fuzzy artificial potential field (FAPF) planning  robots  
多仿生水下机器人协同轨迹规划 会议论文
无, 无, 2021-09
作者:  白舸;  王睿;  王宇;  张天栋;  王硕
Adobe PDF(3630Kb)  |  收藏  |  浏览/下载：233/63  |  提交时间：2022/07/08
轨迹规划  仿生水下机器人  贝塞尔曲线  多机器人协同  实时避障  
DIMSAN: Fast Exploration with the Synergy between Density-based Intrinsic Motivation and Self-adaptive Action Noise 会议论文
, 西安, 2021.5.30-2021.6.5
作者:  Li, Jiayi;  Li, Boyao;  Lu, Tao;  Lu, Ning;  Cai, Yinghao;  Wang, Shuo
Adobe PDF(5599Kb)  |  收藏  |  浏览/下载：184/34  |  提交时间：2022/06/14
SOZIL: Self-Optimal Zero-shot Imitation Learning 期刊论文
IEEE Trans on Cognitive and Developmental System, 2021, 期号: 1, 页码: 1
作者:  Peng Hao;  Tao Lu;  Shaowei Cui;  Junhang Wei;  Yinghao Cai;  Shuo Wang
Adobe PDF(13840Kb)  |  收藏  |  浏览/下载：154/24  |  提交时间：2022/04/08
imitation learning  learning from observation  keyframe demonstration  
Design and Locomotion Control of a Dactylopteridae-Inspired Biomimetic Underwater Vehicle With Hybrid Propulsion 期刊论文
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2021, 页码: 13
作者:  Zhang, Tiandong;  Wang, Rui;  Wang, Yu;  Cheng, Long;  Wang, Shuo;  Tan, Min
Adobe PDF(4770Kb)  |  收藏  |  浏览/下载：260/45  |  提交时间：2022/01/27
Propulsion  DC motors  Robots  Control systems  Servomotors  Underwater vehicles  Sports  Biomimetic underwater vehicle (BUV)  body andor caudal fin (BCF)  median and or paired fin (MPF) hybrid propulsion  central pattern generators (CPGs)  fuzzy proportion integral differential (PID)  
Fast Generation of Chance-Constrained Flight Trajectory for Unmanned Vehicles 期刊论文
IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2021, 卷号: 57, 期号: 2, 页码: 1028-1045
作者:  Chai, Runqi;  Tsourdos, Antonios;  Al Savvaris;  Wang, Shuo;  Xia, Yuanqing;  Chai, Senchun
收藏  |  浏览/下载：161/0  |  提交时间：2021/06/07
Unmanned aerial vehicles  Probabilistic logic  Trajectory optimization  Mathematical model  Aerodynamics  Vehicle dynamics  Trajectory planning  Chance constrained  convex approximation  convex optimization  convexification  trajectory optimization  unmanned vehicle  
Solving Trajectory Optimization Problems in the Presence of Probabilistic Constraints 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2020, 卷号: 50, 期号: 10, 页码: 4332-4345
作者:  Chai, Runqi;  Savvaris, Al;  Tsourdos, Antonios;  Chai, Senchun;  Xia, Yuanqing;  Wang, Shuo
收藏  |  浏览/下载：184/0  |  提交时间：2021/01/07
Planning  Probabilistic logic  Trajectory optimization  Optimal control  Prediction algorithms  Approximation function  chance-constrained  nonlinear programming  probabilistic constraints  trajectory optimization  
Self-modeling Tracking Control of Crawler Fire Fighting Robot Based on Causal Network 会议论文
, 中国澳门, 2019年11月4日至8日
作者:  Chang WK(常文凯);  Li P(李朋);  Yang CY(杨彩云);  Lu T(鲁涛);  Cai YH(蔡莹皓);  Wang S(王硕)
Adobe PDF(5170Kb)  |  收藏  |  浏览/下载：232/53  |  提交时间：2020/10/21
Real-Sim-Real Transfer for Real-World Robot Control Policy Learning with Deep Reinforcement Learning 期刊论文
APPLIED SCIENCES-BASEL, 2020, 卷号: 10, 期号: 5, 页码: 16
作者:  Liu, Naijun;  Cai, Yinghao;  Lu, Tao;  Wang, Rui;  Wang, Shuo
Adobe PDF(6287Kb)  |  收藏  |  浏览/下载：259/63  |  提交时间：2020/06/02
robot  policy learning  reality gap  simulated environment  deep reinforcement learning