Embodied visual exploration is critical for building intelligent visual agents. This paper presents the neural exploration with feature-based visual odometry and tracking-failure-reduction policy(Ne OR), a framework f...Embodied visual exploration is critical for building intelligent visual agents. This paper presents the neural exploration with feature-based visual odometry and tracking-failure-reduction policy(Ne OR), a framework for embodied visual exploration that possesses the efficient exploration capabilities of deep reinforcement learning(DRL)-based exploration policies and leverages feature-based visual odometry(VO) for more accurate mapping and positioning results. An improved local policy is also proposed to reduce tracking failures of feature-based VO in weakly textured scenes through a refined multi-discrete action space, keyframe fusion, and an auxiliary task. The experimental results demonstrate that Ne OR has better mapping and positioning accuracy compared to other entirely learning-based exploration frameworks and improves the robustness of feature-based VO by significantly reducing tracking failures in weakly textured scenes.展开更多
This paper proposes an implicit function based open-loop analysis method to detect the subsynchronous resonance(SSR),including asymmetric subsynchronous modal attraction(ASSMA)and asymmetric subsynchronous modal repul...This paper proposes an implicit function based open-loop analysis method to detect the subsynchronous resonance(SSR),including asymmetric subsynchronous modal attraction(ASSMA)and asymmetric subsynchronous modal repulsion(ASSMR),of doubly-fed induction generator based wind farms(DFIG-WFs)penetrated power systems.As some important parameters of DFIG-WF are difficult to obtain,reinforcement learning and least squares method are applied to identify those important parameters.By predicting the location of closed-loop subsynchronous oscillation(SSO)modes based on the calculation of partial differentials of characteristic equation,both ASSMA and ASSMR can be found.The proposed method in this paper can select SSO modes which move to the right half complex planes as control parameters change.Besides,the proposed open-loop analysis method is adaptive to parameter uncertainty.Simulation studies are carried out on the 4-machine 11-bus power system to verify properties of the proposed method.展开更多
基金supported by the National Natural Science Foundation of China (No.62202137)the China Postdoctoral Science Foundation (No.2023M730599)the Zhejiang Provincial Natural Science Foundation of China (No.LMS25F020009)。
文摘Embodied visual exploration is critical for building intelligent visual agents. This paper presents the neural exploration with feature-based visual odometry and tracking-failure-reduction policy(Ne OR), a framework for embodied visual exploration that possesses the efficient exploration capabilities of deep reinforcement learning(DRL)-based exploration policies and leverages feature-based visual odometry(VO) for more accurate mapping and positioning results. An improved local policy is also proposed to reduce tracking failures of feature-based VO in weakly textured scenes through a refined multi-discrete action space, keyframe fusion, and an auxiliary task. The experimental results demonstrate that Ne OR has better mapping and positioning accuracy compared to other entirely learning-based exploration frameworks and improves the robustness of feature-based VO by significantly reducing tracking failures in weakly textured scenes.
基金supported in part by the State Key Program of National Natural Science Foundation of China under Grant No.U1866210the National Natural Science Foundation of China under Grant No.51807067.
文摘This paper proposes an implicit function based open-loop analysis method to detect the subsynchronous resonance(SSR),including asymmetric subsynchronous modal attraction(ASSMA)and asymmetric subsynchronous modal repulsion(ASSMR),of doubly-fed induction generator based wind farms(DFIG-WFs)penetrated power systems.As some important parameters of DFIG-WF are difficult to obtain,reinforcement learning and least squares method are applied to identify those important parameters.By predicting the location of closed-loop subsynchronous oscillation(SSO)modes based on the calculation of partial differentials of characteristic equation,both ASSMA and ASSMR can be found.The proposed method in this paper can select SSO modes which move to the right half complex planes as control parameters change.Besides,the proposed open-loop analysis method is adaptive to parameter uncertainty.Simulation studies are carried out on the 4-machine 11-bus power system to verify properties of the proposed method.
