To support humans in various environments such as homes, nursing homes, and hospitals, robots need a spatial perception system that enables flexible and rapid recognition of unlearned objects. However, there is a problem that methods that require prior learning cannot fully deal with unlearned objects and that the input of RGB images is easily affected by the lighting conditions of the environment. In this study, a topological map is generated in real-time using Growing Neural Gas, a type of unsupervised self-growing neural network, with 3D point cloud data as the only input. However, in unknown environments, there is a problem that the size of clusters becomes unstable and cannot be detected stably if the region growing method, which checks only labels assigned based on normal vectors, is used to detect clusters such as unknown objects in the topological map. Therefore, we propose a methodology to stably detect clusters in a topological map by introducing the concept of age and a reliability based on fuzzy sets for each node and rejecting unstable nodes with low reliability even if the nodes to which they are connected have the same label. Also, the effectiveness of the proposed methodology is demonstrated by experiments through 3D dynamics simulations.

A robot must exhibit softness so as not to accidentally damage its environment. However, stiffness is also necessary, so that the robot can transmit forces and perform tasks. In soft robotics, it is desirable to be able to switch between two states, namely a flexible state for adapt ion to the environment and a rigid state for the transmission of forces. String jamming mechanisms, which comprise many units connected in a bead-like pattern, have received attention for their ability to switch between flexible and rigid states. In this study, we propose a new design of the string jamming mechanism that enhances the maximum stiffness in the rigid state while maintaining a high fitting performance for the environment in the flexible state. We evaluate the fitting of the mechanism to the environment in a qualitative geometric discussion and compare the performance of the mechanism with that of existing string jamming mechanisms. The results of experiments measuring the maximum stiffness show the usefulness of the proposed mechanism from a quantitative point of view.

In the recently growing field of virtual reality, the virtual grasping sensation remains in its infancy, and the discrepancy between real and virtual grasping has become a problem. This article develops a new haptic glove that presents the sensation of different types of grasping (e.g., power/precision/intermediate grasping) a compliant object. To this end, we develop an exoskeleton that fixes the movements of fingers to present a braking force and a flexible pad that reproduces a wide range of stiffness. By applying the “string jamming mechanism,” our exoskeleton has functions that existing devices do not. In that, it constrains the motion of finger extension/flexion and adduction/abduction with only one actuator and presents the braking force to all surfaces of the finger. In addition, we propose a lightweight and compact variable-stiffness pad that reproduces an extensive stiffness range based on the layer jamming technique. We conducted experiments to evaluate the mechanical performance of the prototype integrating the exoskeleton and the variable-stiffness pad and demonstrated the usefulness of the proposed glove.