This paper proposes a novel 2 DOF rotary-linear actuator driven by pneumatic artificial muscles (PAMs). This simple tubular actuator comprises some PAMs that are braided, glued, and driven periodically to realize rotary and linear motions, which makes the proposed device lighter, cheaper, and easier to sterilize. The elasticity of PAMs and the pneumatic control system without electricity or magnetic power enables the actuator to safely operate not only near a human but also under a magnetic resonance imaging (MRI) environment. In light of these advantages, the proposed actuator is expected to be suitable for a laparoscopic surgical instrument. The operation principles and structure of the actuator are explained in this paper, along with the design of a prototype, which was confirmed to be able to drive a 10[mm] diameter laparoscopy, in linear and rotary directions.
In this study, independent attitude and position control for non-planar multi-rotor helicopter is designed. Conventional multi-rotor helicopters have planar rotor arrangement, non-holonomic and under-actuated properties. Therefore, translational and rotational motion are coupled, and dynamics of attitude and position are not independent. This property is unfavorable on precise position control which is desired for several tasks such as inspection of infrastructure. From this background, we proposed non-planar multi-rotor helicopter which has both simple structure and fully actuated property. In this paper, independent attitude and position control for non-planar multi-rotor helicopter is designed on the basis of optimal control theory. Independence of attitude control and position control are verified by flight experiment. Finally, the superiority of proposed non-planar multi-rotor helicopter compared with conventional one is shown from the point of view of the hovering performance.
In order for autonomous vehicles to drive safely and comfortably, environmental information detected by sensors need to be gathered from wider area and to be more accurate. We can improve data accuracy by fusions of sensor data from not only vehicles but also road infrastructures. Fusion processing is usually performed in a high-performance server (centralized system). However, when the number of sensors is enormous, processing time and communication time for fusions become unacceptable due to high-load and limited capacity of network. And, waiting data-arrivals from all sensors is impractical in such situations. Thus, fusions should be distributed and incrementally updated on each data-arrival. In this paper, we propose a distributed environmental information management system using edge computing and a sensor fusion method.Since the system is composed of geographically distributed edges and a centralized cloud, it can distribute processing costs and communication costs of fusions to the edges and the cloud. The proposal sensor fusion method can incrementally compute intermediate results without waiting for receiving all the environmental information. In comparison experiments with the centralized system, the proposed system improved the efficiency of data processing and reduced the amount of communication data.