The human oral structure contains organs with distinctly different physical properties, such as teeth, gums, and tongues. When food enters the oral cavity, we can recognize the tactile sensation and shape of the object from multiple perspectives through the texture of the teeth and tongue. Therefore, it is possible to regard oral structures as a group of tactile sensors based on these functions. In this study, we developed a soft-matter artificial mouth that can accurately detect subtle differences in texture by creating and combining oral structural organs using polymer materials with different physical properties and mounting them as end-effectors for a robot arm. The same piezoelectric film sensor was embedded inside each organ, making it possible to acquire tactile sensations from the same object as completely different signal waveforms. We tested whether the sensor data obtained from each soft-matter material could be used for excellent object recognition by applying various machine learning methods. In an actual experiment, we learned the waveform data obtained from chewing sweets and snacks, such as rice crackers, and applied machine learning to classify the data, which led to an accuracy rate of over 90%.

Task allocation is a characteristic feature of social insects. This phenomenon is self-organized by workers in a colony without central instructions. Workers perform the necessary tasks while communicating and determining their colony’s local situations or outside environments. They can also allocate the workload of the task to finish quickly. To explain the self-organized phenomenon, we develop a theoretical model that includes the local interaction mechanism among the workers. We extend the fixed response threshold model by introducing the interaction network among workers. The new model is called the interaction network response threshold model. This model analyzes the effects of the interaction network structure on the workload allocation mechanism using the Gini coefficient. We find that the network structure affects the information diffusion process, and the network features affect workload allocation. Our results indicate that communication among the workers in a colony is an essential system for workload allocation.

This paper presents various perspectives on designing and implementing a communication platform for a teleoperated mobile robot. The deployment of a communication network for a mobile robot and the integration of robot components in the developed communication platform are discussed in this article. A Wi-Fi-based communication network has been established, and to secure remote control over long distances, Internet-based communication via the 4G protocol has been launched using a virtual private network setup. Since an unstable network can damage the robot or the surrounding environment, an algorithm has been developed to monitor the state of the network connection through different protocols. The developed algorithm is able to detect network failure independently of the wireless communication technology used and notify the system of any disruptive communication. The robot's primary reactions to connection failure have been programmed to keep the robot under control until the communication with the control station is restored. Various experiments were carried out to validate the performance of the designed algorithm and statistical analysis was performed for each experiment. One of the main contributions of this study is the development of an algorithm for communication between the mobile robot and the control station based on both Wi-Fi and 4G, which is capable to keep the robot behavior safe and reliable in the presence of an unstable network or connection failure.

Self-propelled agents can interact in many different ways, including by perturbing a shared physical environment, e.g, hydrodynamic interactions. We study motile particles that are embedded into a smectic liquid crystal, locally distorting the smectic layer spacing. This results in interactions mediated by a smectic liquid crystal distortion field and corresponds to a form of “active smectic” liquid crystal. We identify several dynamical phases that emerge in different regimes of the smectic stiffness and particle reorientation time. We characterise these as (i) ballistic motion, (ii) clustering and (iii) collective motion where orientational order emerges even thought the system lacks explicit co-alignment. We further identify an order-to-disorder transition on the addition of angular noise.