Electronic skin sensing devices are an emerging technology and have substantial demand in vast practical fields including wearable sensing, robotics, and user‐interactive interfaces. In order to imitate or even outperform the capabilities of natural skin, the keen exploration of materials, device structures, and new functions is desired. However, the very high resistance and the inadequate current switching and sensitivity of reported electronic skins hinder to further develop and explore the promising uses of the emerging sensing devices. Here, a novel resistive cloth‐based skin‐like sensor device is reported that possesses unprecedented features including ultrahigh current‐switching behavior of ≈107 and giant high sensitivity of 1.04 × 104–6.57 × 106 kPa−1 in a low‐pressure region of <3 kPa. Notably, both superior features can be achieved by a very low working voltage of 0.1 V. Taking these remarkable traits, the device not only exhibits excellent sensing abilities to various mechanical forces, meeting various applications required for skin‐like sensors, but also demonstrates a unique competence to facile integration with other functional devices for various purposes with ultrasensitive capabilities. Therefore, the new methodologies presented here enable to greatly enlarge and advance the development of versatile electronic skin applications.

Keeping the information arising from tactile stimulation, such as pressure and texture of objects, is an essential feature for artificial intelligence. This important characteristic is intimately related to physiological behavior in daily activities. In order to develop next‐generation electronic skin for biomimetic prosthetics, repeating the history of a tactile motion is a must‐have function. Here, the first environmentally friendly, low‐cost, and multifunctional device is reported, which can act as a pressure sensor, transistor, as well as memory. This tactile transistor memory is very sensitive to external pressure and can store the pressure information after removing the external stimuli for subsequent manipulation. This work is a key step for the future development of intelligent robotic systems with learning capability.

This paper presents a novel solution of coverage path planning for robotic mowing applications. The planning algorithm is based on the Boustrophedon motions and the rapid Voronoi diagram. The coordinate conversion and the sweeping vector is defined by minimum bounding box and the Voronoi travel paths are designed to reduce the computational cost and execution time compared to conventional heuristic methods. The tracked path is controlled via dynamic feedback in standard lawn mowing robots under robot operating system (ROS). Within ROS, the information exchanging among different tasks of both extensive simulation cases and experimental field tests can be conducted easily. When meeting unknown obstacles, the proposed algorithm can re-plan its paths dynamically. The performance of the proposed algorithm is compared to several conventional coverage algorithms in terms of time efficiency, coverage, repetition, and robustness with respect to both concave and convex shapes. The field tests are conducted to demonstrate that the applicability of the sensor fusion and robustness of the proposed algorithm for the complete coverage tasks by robotic mowers.

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Multiplex PCR methods were established, and M. bovis and M. tuberculosis were identified in feedlot deer in Taiwan. Sequence variations indicated diverse sources of both mycobacterial species.