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Control Policy Learning Design for Vehicle Urban Positioning via beidou navigation

Journal of Systems Science & Complexity 2024年第1期37卷 114-135页

作者： QIN Yahang ZHANG Chengye CHEN Ci XIE Shengli LEWIS Frank L School of Automation Guangdong University of TechnologyGuangzhou 510006China Guangdong Key Laboratory of IoT Information Technology Guangzhou 510006China. Center for Intelligent Batch Manufacturing Based on IoT Technology Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing Ministry of EducationGuangzhou 510006China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou 510006China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangzhou 510006China. UTA Research Institute The University of Texas at ArlingtonFort WorthTX 76019USA

This paper presents a learning-based control policy design for point-to-point vehicle positioning in the urban environment via beidou *** navigating in urban canyons,the multipath effect is a kind of interference that causes the navigation signal to drift and thus imposes severe impacts on vehicle localization due to the reflection and diffraction of the beidou ***,the authors formulated the navigation control system with unknown vehicle dynamics into an optimal control-seeking problem through a linear discrete-time system,and the point-to-point localization control is modeled and handled by leveraging off-policy reinforcement learning for feedback *** proposed learning-based design guarantees optimality with prescribed performance and also stabilizes the closed-loop navigation system,without the full knowledge of the vehicle *** is seen that the proposed method can withstand the impact of the multipath effect while satisfying the prescribed convergence rate.A case study demonstrates that the proposed algorithms effectively drive the vehicle to a desired setpoint under the multipath effect introduced by actual experiments of beidou navigation in the urban environment.

关键词： beidou navigation multipath effect prescribed convergence rate reinforcement learning urban localization.

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Analysis and Decision of Optimal Path of Forest Disaster Patrol Based on beidou navigation

Analysis and Decision of Optimal Path of Forest Disaster Pat...

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第三十九届中国控制会议

作者： Jinyi Ma Lifu Hu Bini Pan Zhongzhi Li Yuqing Tian Chunyu Chen Department of Automation Shenyang Aerospace University Department of Computer Science Shenyang Aerospace University

In this paper, a forest data map returned by beidou satellites is combined with classic theories such as graph theory and mathematical planning, and the Dijkstra algorithm, which is the most widely used in practice, is the core to propose a forest patrol optimal path planning model. And by comparing with the Floyd path planning algorithm, the correctness and implementability of the model are verified. The experimental results show that the algorithm effectively solves the problem of optimal path decision from a patrol base to an arbitrary forest area in a certain forest, and verifies that the Dijkstra algorithm can be accurately and reliably applied to the optimal path planning field of forest patrol.

关键词： beidou navigation Optimal path planning Forest patrol Dijkstra algorithm

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