The thermodynamic equilibrium assumption often invoked in modeling ion migration in solid-state materials remains insufficient to capture the true migration behavior of Li ions, particularly in less-crystalline superionic conductors that exhibit anomalously high Li ion conductivity. Such materials challenge classical frameworks and necessitate a lattice dynamics-based perspective that explicitly accounts for nonequilibrium phonon interactions and transient structural responses. Here, we uncover a phonon-governed Li ion migration mechanism in garnet-structured superionic conductors by comparing Ta-doped Li_6.6La₃Zr_1.6Ta_0.4O₁₂ (LLZTO4) to its undoped analogue, Li_6.24La₃Zr₂Al_0.24O_11.98 (LLZO). Through a synergistic combination of terahertz time-domain spectroscopy (THz-TDS), ⁷Li magic-angle spinning nuclear magnetic resonance (MAS-NMR), and Raman spectroscopy, we show that Ta doping softens the host lattice and enhances anharmonic phonons, enabling collective, thereby multi-ion migration beyond the limit of single-ion hopping models. This lattice softening induces a dynamically disordered energy landscape that lowers activation barriers and yields Li ion conductivities approaching those of liquid electrolytes. Our findings demonstrate that anharmonic lattice vibrations can serve as the driving force for ultrafast Li ion migration in solid electrolytes. This paradigm shift establishes a fundamental link between lattice thermodynamics and superionic conduction, providing a conceptual and experimental framework for the design of highly conductive solid-state electrolytes.

Mobile robots operating in outdoor environments face the challenge of navigating various terrains with different degrees of difficulty. Therefore, traversability estimation is crucial for safe and efficient robot navigation. Current approaches utilize a robot's driving experience to learn traversability in a self-supervised fashion. However, providing sufficient and diverse experience to the robot is difficult in many practical applications. In this paper, we propose a self-supervised traversability learning method that adapts to challenging terrains with limited prior experience. One key aspect is to enable prioritized learning of scarce yet high-risk terrains by using a risk-sensitive approach. To this end, we train a neural network through a risk-aware instance weighting scheme. Another key aspect is to leverage traversability pseudo-labels on the basis of a self-training scheme. The proposed confidence-regularized self-training generates high-quality pseudo-labels, thereby achieving reliable data augmentation for unexperienced terrains. The effectiveness of the proposed method is verified in extensive real-world experiments, ranging from structured urban environments to complex rugged terrains.

The thermodynamic equilibrium assumption often invoked in modeling ion migration in solid-state materials remains insufficient to capture the true migration behavior of Li ions, particularly in less-crystalline superionic conductors that exhibit anomalously high Li ion conductivity. Such materials challenge classical frameworks and necessitate a lattice dynamics-based perspective that explicitly accounts for nonequilibrium phonon interactions and transient structural responses. Here, we uncover a phonon-governed Li ion migration mechanism in garnet-structured superionic conductors by comparing Ta-doped Li_6.6La₃Zr_1.6Ta_0.4O₁₂ (LLZTO4) to its undoped analogue, Li_6.24La₃Zr₂Al_0.24O_11.98 (LLZO). Through a synergistic combination of terahertz time-domain spectroscopy (THz-TDS), ⁷Li magic-angle spinning nuclear magnetic resonance (MAS-NMR), and Raman spectroscopy, we show that Ta doping softens the host lattice and enhances anharmonic phonons, enabling collective, thereby multi-ion migration beyond the limit of single-ion hopping models. This lattice softening induces a dynamically disordered energy landscape that lowers activation barriers and yields Li ion conductivities approaching those of liquid electrolytes. Our findings demonstrate that anharmonic lattice vibrations can serve as the driving force for ultrafast Li ion migration in solid electrolytes. This paradigm shift establishes a fundamental link between lattice thermodynamics and superionic conduction, providing a conceptual and experimental framework for the design of highly conductive solid-state electrolytes.

Mobile robots operating in outdoor environments face the challenge of navigating various terrains with different degrees of difficulty. Therefore, traversability estimation is crucial for safe and efficient robot navigation. Current approaches utilize a robot's driving experience to learn traversability in a self-supervised fashion. However, providing sufficient and diverse experience to the robot is difficult in many practical applications. In this paper, we propose a self-supervised traversability learning method that adapts to challenging terrains with limited prior experience. One key aspect is to enable prioritized learning of scarce yet high-risk terrains by using a risk-sensitive approach. To this end, we train a neural network through a risk-aware instance weighting scheme. Another key aspect is to leverage traversability pseudo-labels on the basis of a self-training scheme. The proposed confidence-regularized self-training generates high-quality pseudo-labels, thereby achieving reliable data augmentation for unexperienced terrains. The effectiveness of the proposed method is verified in extensive real-world experiments, ranging from structured urban environments to complex rugged terrains.

Abstract Accurate and robust localization is essential for autonomous mobile robots. Map matching based on Light Detection and Ranging (LiDAR) sensors has been widely adopted to estimate the global location of robots. However, map‐matching performance can be degraded when the environment changes or when sufficient features are unavailable. Indiscriminately incorporating inaccurate map‐matching poses for localization can significantly decrease the reliability of pose estimation. This paper aims to develop a robust LiDAR‐based localization method based on map matching. We focus on determining appropriate weights that are computed from the uncertainty of map‐matching poses. The uncertainty of map‐matching poses is estimated by the probability distribution over the poses. We exploit the normal distribution transform map to derive the probability distribution. A factor graph is employed to combine the map‐matching pose, LiDAR‐inertial odometry, and global navigation satellite system information. Experimental verification was successfully conducted outdoors on the university campus in three different scenarios, each involving changing or dynamic environments. We compared the performance of the proposed method with three LiDAR‐based localization methods. The experimental results show that robust localization performances can be achieved even when map‐matching poses are inaccurate in various outdoor environments. The experimental video can be found at https://youtu.be/L6p8gwxn4ak .

Autonomous driving systems encounter various uncertainties in real-world environments, many of which are difficult to represent in maps. Among them, accumulated snow poses a unique challenge since its shape and volume gradually change over time. If accumulated snow is included in a map, it leads to two main problems. First, during long-term driving, discrepancies between the actual and mapped environments, caused by melting snow, can significantly degrade localization performance. Second, the inclusion of large amounts of accumulated snow in the map can cause registration errors between sessions, thereby hindering accurate map updates. To address these issues, we propose a mapping strategy specifically designed for snow-covered environments. The proposed method first detects and removes accumulated snow using a deep learning-based approach. The resulting snow-free data are then used for map updating, and the ground information occluded by snow is subsequently restored. The effectiveness of the proposed method is validated with data collected in real-world snow-covered environments. Experimental results demonstrate that the proposed method achieves 78.6% IoU for snow detection and reduces map alignment errors by 12.5% (RMSE) and 15.6% (Chamfer Distance) on average, contributing to maintaining map quality and enabling long-term autonomous driving in snow-covered environments.

In this study, gearbox radiated noise was successfully reduced through housing shape optimization. First, dynamic and structural-acoustic coupled analysis models of an agricultural UTV gearbox were developed. Then, the test equipment was configured to match that of the simulation model, and a test was conducted. The analysis and test results showed errors within 0.1 dB for vibration and 0.2 dB(A) for noise, indicating that the models were reliable. The housing design was then optimized using topology optimization based on the developed structural-acoustic coupling analysis model. The sound pressure level around the housing was used as an objective function for topology optimization. The optimal distribution of materials for the housing was also derived to reduce the radiated noise. Lastly, the housing rib was designed based on the optimization result, and an improvement in the radiated noise by approximately 2.43 dB(A) was confirmed in the operation area.

Vehicle localization using mounted sensors is an essential technology for various applications, including autonomous vehicles and road mapping. Achieving high positioning accuracy through the fusion of low-cost sensors is a topic of considerable interest. Recently, applications based on crowdsourced data from a large number of vehicles have received significant attention. Equipping standard vehicles with low-cost onboard sensors offers the advantage of collecting data from multiple drives over extensive road networks at a low operational cost. These vehicle trajectories and road observations can be utilized for traffic surveys, road inspections, and mapping. However, data obtained from low-cost devices are likely to be highly inaccurate. On urban roads, unlike highways, complex road structures and GNSS signal obstructions caused by buildings are common. This study proposes a reliable vehicle localization method using a large amount of crowdsourced data collected from urban roads. The proposed localization method is designed with consideration for the high inaccuracy of the data, the complexity of road structures, and the partial use of high-definition (HD) maps that account for environmental changes. The high inaccuracy of sensor data affects the reliability of localization. Therefore, the proposed method includes a reliability assessment of the localized vehicle poses. The performance of the proposed method was evaluated using data collected from buses operating in Seoul, Korea. The data used for the evaluation were collected 18 months after the creation of the HD maps.

In traditional Multi-Agent Path Finding (MAPF) problems, environments are typically represented as grid graphs. However, due to the high computational cost in continuous time domains, recent studies have employed roadmap graphs. This study assesses how different methods for generating roadmap graphs affect path planning. Through simulations in static environments such as factories and warehouses, we observed a balance in performance based on the attributes of each method. Notably, overlooking rotational costs in graph searches can lead to significantly higher path costs. Incorporating these insights, we devised graph generation techniques that markedly enhance path planning efficiency.

The conjunction fallacy is the well-documented reasoning error on which people rate a conjunction A&B as more probable than one of its conjuncts, A. Many explanations appeal to the fact that B has a high probability in the given scenarios, but Tentori et al. (2013) have challenged such approaches. They report experiments suggesting that degree of confirmation—rather than probability—is the central determinant of the conjunction fallacy. In this paper, we have two goals. First, we address a confound in Tentori et al.’s (2013) experiments: they failed to control for the fact that in their stimuli where B is confirmed, it is also conversationally relevant in the sense that it fits with the topic or question under discussion (Roberts, 2012). Conversely, when B has a high probability but is not confirmed, it is conversationally irrelevant. Consequently, it is possible that conversational relevance, rather than confirmation, is responsible for the differences they found between confirmed and probable hypotheses. Second, inspired by recent theoretical work, we aim to give the first empirical investigation of the hypothesis that this type of conversational relevance on its own—independently of degree of confirmation—can be an important factor in the conjunction fallacy. We report on two experiments that vary Tentori et al.’s (2013) design by making B relevant without changing its degree of probability or confirmation. We found that doing so increases the rate of the conjunction fallacy, suggesting that relevance plays an important role in the conjunction fallacy.

We introduce purely visual paradigms that convey the logical structure of illusory inferences from disjunction: (a AND b) OR c, a |- b. Although the logical information was conveyed entirely via non-linguistic means, we found that the visual paradigms induce reasoning fallacies, though less attractive than their linguistic counterparts. The visual paradigms highlight the role of alternative-based reasoning, or question-answer dynamics, as they control for narrowly interpretive processes that confound the study of their linguistic counterparts. To our knowledge, this is the first work to develop visual paradigms that represent reasoning fallacies committed by adults and involve multiple logical operators non-trivially embedded. Previous studies focused on pre-verbal children or non-human animals, and for this reason limited the scope of research to visually representing logically simple, valid inferences.