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Proceedings of the 7th International Conference on Robotics and Artificial Intelligence最新文献

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Attitude Control Method of Six Degree of Freedom Autonomous Underwater Vehicle Based on RBF Neural Network 基于RBF神经网络的六自由度自主水下航行器姿态控制方法
Proceedings of the 7th International Conference on Robotics and Artificial Intelligence Pub Date : 2021-11-19 DOI: 10.1145/3505688.3505700
Xuewen Zhu, Fuxiao Tan
{"title":"Attitude Control Method of Six Degree of Freedom Autonomous Underwater Vehicle Based on RBF Neural Network","authors":"Xuewen Zhu, Fuxiao Tan","doi":"10.1145/3505688.3505700","DOIUrl":"https://doi.org/10.1145/3505688.3505700","url":null,"abstract":"This paper addresses a new adaptive control method based on radial basis function (RBF) neural network to control the attitude of the autonomous underwater vehicle. The mathematical model of the autonomous underwater vehicle is constructed and its kinematic model and dynamic model are established. The Lyapunov theory is used to analyze the convergence of the estimations. The advantages of this neural network are verified through simulation, which is helpful and enlightening for the design of the control system of the underwater vehicle.","PeriodicalId":375528,"journal":{"name":"Proceedings of the 7th International Conference on Robotics and Artificial Intelligence","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130842316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Benchmarking Lane-changing Decision-making for Deep Reinforcement Learning 基于深度强化学习的变道决策基准
Proceedings of the 7th International Conference on Robotics and Artificial Intelligence Pub Date : 2021-09-22 DOI: 10.1145/3505688.3505693
Junjie Wang, Qichao Zhang, Dongbin Zhao
{"title":"Benchmarking Lane-changing Decision-making for Deep Reinforcement Learning","authors":"Junjie Wang, Qichao Zhang, Dongbin Zhao","doi":"10.1145/3505688.3505693","DOIUrl":"https://doi.org/10.1145/3505688.3505693","url":null,"abstract":"The development of autonomous driving has attracted extensive attention in recent years, and it is essential to evaluate the performance of autonomous driving. However, testing on the road is expensive and inefficient. Virtual testing is the primary way to validate and verify self-driving cars, and the basis of virtual testing is to build simulation scenarios. In this paper, we propose a training, testing, and evaluation pipeline for the lane-changing task from the perspective of deep reinforcement learning. First, we design lane change scenarios for training and testing, where the test scenarios include stochastic and deterministic parts. Then, we deploy a set of benchmarks consisting of learning and non-learning approaches. We train several state-of-the-art deep reinforcement learning methods in the designed training scenarios and provide the benchmark metrics evaluation results of the trained models in the test scenarios. The designed lane-changing scenarios and benchmarks are both opened to provide a consistent experimental environment for the lane-changing task1 .","PeriodicalId":375528,"journal":{"name":"Proceedings of the 7th International Conference on Robotics and Artificial Intelligence","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115561689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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