The application of vision-guided multiwarehouse automated guided vehicles (AGVs) is becoming more extensive. This article mainly studies the position measurement and corresponding path planning of AGV guided by visual sensors. An improved vehicle-mounted visual sensor model is proposed, which effectively reduces the cost of the sensor and achieves position prediction. The proposed manual coding landmark significantly improves the decoding speed and accuracy while ensuring the information coding capacity. An improved heuristic path planning algorithm combined with the visual sensor system is implemented, considering the planning effectiveness and directionality. Finally, simulations and experiments verify the reliability of the visual guidance method in position measurement and the advancement of the planning strategy. Compared with the traditional 2-D code and bottom visual model, the improved visual sensor position measurement effectively reduces motion blur and corresponding pose deviation. The decoding speed and accuracy are improved by more than 41.21% and 53.85%, respectively. Planning performance comparison is achieved between the same type of heuristic algorithms (A*, D*, and algorithm deployment). The significant improvement in core system indicators includes task efficiency (up to 70.02% improvement), parking time (up to 72% reduction), deadlock (up to 85.05% reduction), and planning time (up to 89.39% reduction).

Low‐rank matrices play a central role in modelling and computational methods for signal processing and large‐scale data analysis. Real‐world observed data are often sampled from low‐dimensional subspaces, but with sample‐specific corruptions (i.e. outliers) or random noises. In many applications where low‐rank matrices arise, these matrices cannot be fully sampled or directly observed, and one encounters the problem of recovering the matrix given only incomplete and indirect observations. The authors aim to recover a low‐rank component from incomplete and indirect observations and correct the possible errors. A new low‐rank matrix recovery formula based on generalised Tikhonov regularisation and its solution algorithm are proposed. The proposed method determines the low‐rank component for performing matrix recovery from highly corrupted observations. The authors’ recommended algorithm reduces not only the outliers but also random corruptions in the recovering task. The experimental results obtained using both synthetic and real application data demonstrate the efficacy of the proposed method.

The main goal of this paper is to present a methodology to design multi-objective H∞/r -stability interval estimation for linear discrete-time systems affected by bounded, but unknown disturbances. Compared with H∞ norm criterion that is used to reject the influence of external disturbances on the output under the worst scenario, multi-objective H∞/r-stability interval observer takes pole-placement into account, the poles of the system matrix of the observes are configured as close as possible to the origin. This is to ensure not only a relatively fast convergence characteristic but also the minimum interval width. Finally, an illustrative example of a DC servo motor highlights the performance of our methodology.

Sustaining learner motivation remains a persistent challenge in higher education Japanese-language e-learning, where learners often study with limited social presence and personalized encouragement. This paper proposes a preliminary Design Framework for Motivational Robots in E-Learning (DFMRE), derived through a retrospective, theory-guided synthesis of two previously published empirical studies on robot-assisted Japanese-language learning. The synthesis interprets the prior findings through Self-Determination Theory and self-efficacy theory and formulates five candidate design principles: human-affine compact hardware, multi-level learner-selectable gestures, calibrated vocal encouragement, learner-initiated interaction protocol, and content-independent system integration. To provide an initial external check, nine domain experts with diverse backgrounds in education, educational technology, human–robot interaction, and related fields provided a preliminary appraisal of the principles in terms of clarity, feasibility, transferability, and overall usefulness. The framework received broadly favorable ratings, including a mean overall usefulness score of 4.33 on a 5-point scale, while expert comments highlighted the need for clearer operational definitions and flexible interaction modes. Because the empirical base consists of two small-sample Japanese-language learning studies conducted at one institution using one robot platform, DFMRE should be read as an early, context-grounded, falsifiable proposal rather than as a confirmed model for higher education e-learning in general.

Controlled releases can reveal downstream response timing when calibrated hydraulic routing is unavailable, but sparse stage-only records limit what can be inferred. We analyze one principal 2026 controlled release on the Satsunai River, Japan, using dam outflow and two downstream water-level series. We ask whether dam-referenced lags agree with a direct station-to-station estimate, whether the reach-scale timing persists under alternative preprocessing and dependence assumptions, and what timing can be assigned conditionally to an intermediate ungauged location. Cross-correlation maxima occurred at 3.67 h at Kamisatsunai and 8.00 h at Dainikawabashi; their 4.33 h difference matched the full-record station-to-station maximum and was close to the 4.67 h release-window result. Across detrending and differencing transformations, endpoint separation remained 3.67–4.33 h. Expanded 1–24 h moving-block bootstrap sensitivity yielded a lag-difference envelope of 4.17–4.50 h, corresponding to an apparent celerity of 1.30–1.41 m/s. Linear interpolation gave 5.84 h at Nakajima Shinbashi, with a conditional percentile-interval envelope of 5.51–6.43 h. Taken together, these estimates give a consistent event-specific description of bulk hydrograph alignment. Additional events and hydraulic observations are needed to assess transferability and spatial model-form uncertainty.