The highly infectious and serious nature of coronavirus disease 2019 (COVID-19) has highlighted the need for hospital space disinfection technology and the prevention of human exposure to pathogenic environments. This research developed novel chlorine dioxide (ClO ₂ ) sterilization technology to reduce bacteria and viruses in the air and on surfaces. A smart sterilization robot system was also developed to spray disinfectants in operating theaters or patients’ rooms, designed according to the results of controlled experiments and the requirements for hospital disinfection. The system was built incorporated a semi-automatic remote-controlled module and an automatic intelligent disinfection function; that is, it could operate independently according to specific epidemic prevention strategies, which were implemented using a combination of Internet of Things (IoT) applications and a gesture recognition function. The elimination of Escherichia coli ( E. coli ) bacteria on sample plates was 99.8 % effective. This paper reviews the evolution of various disinfection technologies and describes a disinfection robot system in detail.

The accurate state of charge (SOC) estimation of the LiFePO₄ battery packs used in robot applications is required for better battery life cycle, performance, reliability, and economic issues. In this paper, a new SOC estimation method, “Modified ECE + EKF”, is proposed. The method is the combination of the modified Equivalent Coulombic Efficiency (ECE) method and the Extended Kalman Filter (EKF) method. It is based on the zero-state hysteresis battery model, and adopts the EKF method to correct the initial value used in the Ah counting method. Experimental results show that the proposed technique is superior to the traditional techniques, such as ECE + EKF and ECE + Unscented Kalman Filter (UKF), and the accuracy of estimation is within 1%.

Robots frequently need to work in human environments and handle many different types of objects. There are two problems that make this challenging for robots: human environments are typically cluttered, and the multi-finger robot hand needs to grasp and to lift objects without knowing their mass and damping properties. Therefore, this study combined vision and robot hand real-time grasp control action to achieve reliable and accurate object grasping in a cluttered scene. An efficient online algorithm for collision-free grasping pose generation according to a bounding box is proposed, and the grasp pose will be further checked for grasp quality. Finally, by fusing all available sensor data appropriately, an intelligent real-time grasp system was achieved that is reliable enough to handle various objects with unknown weights, friction, and stiffness. The robots used in this paper are the NTU 21-DOF five-finger robot hand and the NTU 6-DOF robot arm, which are both constructed by our Lab.

We report the findings for the first teleseismic six degree‐of‐freedom (6‐DOF) measurements including three components of rotational motions recorded by a sensitive rotation‐rate sensor (model R‐1, made by eentec) and three components of translational motions recorded by a traditional seismometer (STS‐2) at the NACB station in Taiwan. The consistent observations in waveforms of rotational motions and translational motions in sections of Rayleigh and Love waves are presented in reference to the analytical solution for these waves in a half space of Poisson solid. We show that additional information (e.g., Rayleigh wave phase velocity, shear wave velocity of the surface layer) might be exploited from six degree‐of‐freedom recordings of teleseismic events at only one station. We also find significant errors in the translational records of these teleseismic surface waves due to the sensitivity of inertial translation sensors (seismometers) to rotational motions. The result suggests that the effects of such errors need to be counted in surface wave inversions commonly used to derive earthquake source parameters and Earth structure.

SLAM (Simultaneous Localization and Mapping), primarily relying on camera or LiDAR (Light Detection and Ranging) sensors, plays a crucial role in robotics for localization and environmental reconstruction. This paper assesses the performance of two leading methods, namely ORB-SLAM3 and SC-LeGO-LOAM, focusing on localization and mapping in both indoor and outdoor environments. The evaluation employs artificial and cost-effective datasets incorporating data from a 3D LiDAR and an RGB-D (color and depth) camera. A practical approach is introduced for calculating ground-truth trajectories and during benchmarking, reconstruction maps based on ground truth are established. To assess the performance, ATE and RPE are utilized to evaluate the accuracy of localization; standard deviation is employed to compare the stability during the localization process for different methods. While both algorithms exhibit satisfactory positioning accuracy, their performance is suboptimal in scenarios with inadequate textures. Furthermore, 3D reconstruction maps established by the two approaches are also provided for direct observation of their differences and the limitations encountered during map construction. Moreover, the research includes a comprehensive comparison of computational performance metrics, encompassing Central Processing Unit (CPU) utilization, memory usage, and an in-depth analysis. This evaluation revealed that Visual SLAM requires more CPU resources than LiDAR SLAM, primarily due to additional data storage requirements, emphasizing the impact of environmental factors on resource requirements. In conclusion, LiDAR SLAM is more suitable for the outdoors due to its comprehensive nature, while Visual SLAM excels indoors, compensating for sparse aspects in LiDAR SLAM. To facilitate further research, a technical guide was also provided for the researchers in related fields.