the distressing interfacial issues is critical to realize the smooth operation and long lifespan of SSAMBs.The unsatisfactory interface is mainly caused by the chemical mismatch and the rigid contact between the solid electrolyte and the metal anode. [13,14] Researchers have proposed various methods to boost the robust solid electrolyte/Li or Na metal interface for reduced interfacial impedance, improved critical current densities, and long-term cycling stability. [15][16][17][18][19][20][21] Among them, surface modification of the solid electrolyte proves one of the most effective ways as it introduces an active interphase to mitigate the difference between the alkaline metal and the solid electrolyte. [17,22] Functional coatings including

Achieving satisfactory performance for a solid‐state Na‐metal battery (SSNMB) with an inorganic solid electrolyte (SE), especially under freezing temperatures, poses a challenge for stabilizing a Na‐metal anode. Herein, this challenge is addressed by utilizing a Natrium super ionic conductor (NASICON) NASICON‐type solid electrolyte, enabling the operation of a rechargeable SSNMB over a wide temperature range from −20 to 45 °C. The interfacial resistance at the Na metal/SE interface is only 0.4 Ω cm2 at 45 °C and remains below 110 Ω cm2 even at −20 °C. Remarkably, long‐term Na‐metal plating/stripping cycles lasting over 2000 h at −20 °C are achieved with minimal polarization voltages at 0.1 mA cm−2. Further analysis reveals the formation of a uniform Na3−xCaxPO4 interphase layer at the interface, which significantly contributes to the exceptional interfacial performance observed. By employing a Na3V1.5Al0.5(PO4)3 cathode, the full battery system demonstrates excellent adaptability to low temperatures, exhibiting a capacity of 80 mA h g−1 at −20 °C over 50 cycles and retaining a capacity of 108 mAh g−1 (88.5% of the capacity at 45 °C) at 0 °C over 275 cycles. This research significantly reduces the temperature threshold for SSNMB operation and paves the way toward solid‐state batteries suitable for all‐season applications.

<abstract> <p>This paper proposes an anti-rotation template matching method based on a portion of the whole pixels. To solve the problem that the speed of the original template matching method based on NCC (Normalized cross correlation) is too slow for the rotated image, a template matching method based on Sub-NCC is proposed, which improves the anti-jamming ability of the algorithm. At the same time, in order to improve the matching speed, the rotation invariant edge points are selected from the rotation invariant pixels, and the selected points are used for rough matching to quickly screen out the unmatched areas. The theoretical analysis and experimental results show that the accuracy of this method is more than 95%. For the search map at any angle with the resolution at the level of 300,000 pixel, after selecting the appropriate pyramid series and threshold, the matching time can be controlled to within 0.1 s.</p> </abstract>

Purpose Autonomous robots must be able to understand long-term manipulation tasks described by humans and perform task analysis and planning based on the current environment in a variety of scenes, such as daily manipulation and industrial assembly. However, both classical task and motion planning algorithms and single data-driven learning planning methods have limitations in practicability, generalization and interpretability. The purpose of this work is to overcome the limitations of the above methods and achieve generalized and explicable long-term robot manipulation task planning. Design/methodology/approach The authors propose a planning method for long-term manipulation tasks that combines the advantages of existing methods and the prior cognition brought by the knowledge graph. This method integrates visual semantic understanding based on scene graph generation, regression planning based on deep learning and multi-level representation and updating based on a knowledge base. Findings The authors evaluated the capability of this method in a kitchen cooking task and tabletop arrangement task in simulation and real-world environments. Experimental results show that the proposed method has a significantly improved success rate compared with the baselines and has excellent generalization performance for new tasks. Originality/value The authors demonstrate that their method is scalable to long-term manipulation tasks with varying complexity and visibility. This advantage allows their method to perform better in new manipulation tasks. The planning method proposed in this work is meaningful for the present robot manipulation task and can be intuitive for similar high-level robot planning.

Autonomous indoor service robots are affected by multiple factors when they are directly involved in manipulation tasks in daily life, such as scenes, objects, and actions. It is of self-evident importance to properly parse these factors and interpret intentions according to human cognition and semantics. In this study, the design of a semantic representation framework based on a knowledge graph is presented, including (1) a multi-layer knowledge-representation model, (2) a multi-module knowledge-representation system, and (3) a method to extract manipulation knowledge from multiple sources of information. Moreover, with the aim of generating semantic representations of entities and relations in the knowledge base, a knowledge-graph-embedding method based on graph convolutional neural networks is proposed in order to provide high-precision predictions of factors in manipulation tasks. Through the prediction of action sequences via this embedding method, robots in real-world environments can be effectively guided by the knowledge framework to complete task planning and object-oriented transfer.