7 accepted papers
The McKibben muscle, widely employed in antagonistic drive robots, exhibits approximately 20% contraction under pneumatic pressure but lacks crucial passive extensibility. Previous attempts to achieve passive extensibility resulted in reduced overall contraction ratios due to the series connection o
The McKibben muscle can produce a high force-to-mass ratio, beneficial for various applications in the soft mechatronics field. The thin McKibben muscle, which has a small diameter, has the advantage of a high force-to-mass ratio and sufficient flexibility for use in a bent state. This flexibility p
A tensegrity-based system is a promising approach for dynamic exploration of uneven, unpredictable, and confined environments. However, implementing such systems presents challenges in state recognition. In this study, we introduce a 6-strut tensegrity structure integrated with 24 multimodal strain
Significant progress has been achieved in the development of tensegrity robots with rolling capabilities. However, because rolling robots operate passively due to gravity, they are limited to certain environments. So, our aim is to enhance the versatility of tensegrity robots by modularizing a six-b
Tensegrity structures have been actively studied in recent years because they are lightweight, compliant, and flexible, which are properties not typically found in conventional robots. This structure can be modularized to create soft robots that operate in unknown environments such as cave or space
The primary role of a robot exploring an unknown space is to investigate the state and the spatial shape of the environment. We have designed a soft robot that aims to move forward in an unknown space as it recognizes and adapts to the spatial shape of the environment. We previously reported that so
Computer simulations have facilitated our understanding of the dynamic behavior of the brain and the effect of the medical treatment such as deep brain stimulation. For improving the simulation model, it is essential to develop a method for optimizing parameters of a neuron model from available expe…