Today, soft robots are getting increasing amounts of attention from robotics researchers. Traditional rigid linkage system robots have drawbacks, including that they are unsafe and heavy. These drawbacks are not easy to overcome without changing the basic principles of actuators and mechanism systems. Inspired by soft animals, some researchers have tried to create soft robots, which are totally different from traditional hard robots. Recently, a new soft robot concept called Honeycomb PneuNets (HPN) has been proposed, which is supposed to be more flexible and dexterous than traditional robots. In this paper, we first analysed this concept from a bionic view. Then, we fabricated a prototype and used it to achieve flexible movements. In our experiments, the robot showed that it can elongate and bend in any direction, and had excellent elongation and bending curvature rates. Furthermore, it could change its stiffness during the various movements. These flexible deformation abilities of HPN robots imply that it could be a new promising technology in soft robotics.

A new Honeycomb PneuNets (HPN) soft gripper and its grasp strategy are proposed. The theoretical model of the soft gripper has infinite degrees of freedom, so it can fit the surface of the objects completely. In order to calculate the final grasp state, the grasp process is simulated at every selected grasp point according to the characteristics of motion and grasp. For every final grasp state, the decision point is taken to get the feasible solution set using the relative form closure theory. For each feasible solution, the evaluation function for the HPN soft gripper is calculated. Then the best solution is selected to get the best grasp plan. The experimental results show that the new soft gripper and its grasp strategy can greatly improve the grasp success rate on common geometrical objects.

介绍了国外军用航空器搭载的红外搜索跟踪系统、红外对抗系统、导弹及对应的制冷机匹配情况.对目前国外空军使用的在产或在研的各种整体集成式斯特林制冷机、线性斯特林制冷机的特点进行了总结,指出制冷机的环境适应性与可靠性是制冷机最重要的发展特点.

With the developing of sensor technology, smart materials and devices get increasing attention. Unlike desktop computing, smart devices typically have limited computing power and often need to react in real-time. So it's difficult to directly apply complex algorithms on it due to the restriction in computational capability. Planning algorithm is one of these complex algorithms. In this paper, we proposed a new planning algorithm based on Rapid-exploring Random Trees (RRTs), which are currently widely used in robotics. In this paper, firstly we introduced the RRT algorithm and its analysis. Secondly, based on the analysis, we proposed an improved algorithm Exponential Backoff sampling Rapid-exploring Random Tree (EB_RRT). The main idea in EB_RRT is to estimate the most efficacious sampling area to avoid useless sampling. In our experiments, we constructed a map with randomly placed obstacles. In the map, EB_RRT and several currently best algorithms were compared through solving a path planning problem. The results showed that our method makes an order of magnitude improvement than the existing ones and the low time consuming characteristic makes it a promising technology for smart devices.

Soft robot is becoming a current focus for its inherently compliance and human-friendly interacting with the real world. But most soft robots are designed and fabricated with intuition and empiricism only, which lack of systematic assessment such as force and deformable analysis before fabricating. Before choosing proper soft materials and processing the craft, the experiments of the soft robots can hardly be set up. In this paper, we construct a model of Honeycomb Pneumatic Finger (HPF) with honeycomb pneumatic network embedded which overcomes the shortcoming of embedded rectangular unit. In the meantime, a pressure analysis model is built for the purpose of physical simulation. Based on the model, without choosing any real materials and fabricating, we focus on exploring the correlations between the pressure and the geometrical shapes in physics simulation. Furthermore, we construct a virtual hand consisting of one rigid palm and four HPFs which is simulated with Bullet Physics Engine. By changing the pressure of the corresponding honeycomb units of each finger, the hand can grasp a ball smoothly and lift it up. At last, the deviation between the mathematical analysis and the physical simulation is discussed.

Soft robots are robots made of soft materials and actuators. Previously we proposed the HPN (Honeycomb PneuNets) Robot, where PneuNets were placed as actuators into honeycomb shaped elastomer. In this paper, we present some progress of this effort. A random search algorithm is applied to plan the obstacle-avoid movements of an HPN robot. We test it through several cases, and the results showed that the algorithm can work effectively. We introduce an HPN robot prototype, which is made of RTV-2 silicone rubber. Preliminary experiments showed that some good expansion rate and flexibility can be achieved. A piston and soft body simulation model of HPN robots is also presented, which can mimic the basic behaviors of the HPN robot.

In recent years, Soft Robotics becomes a research hotspot. A soft robot is usually made of elastic materials, and thus has better adaptability and safety to the environment than a rigid robot. These advantages offer us a new opportunity to attack some fundamental challenges faced by traditional robots. Most of previous studies about soft robots focus on clarifying the deformation characteristics of the flexible materials used. In order to pursue a large expansion rate, the stiffness of the soft materials is usually very low, which brings a consequence that these soft robots are too soft to maintain its shape or to resist external forces. Inspired by the honeycomb structure, this paper proposes a honeycomb pneumatic network (HPN) robot, which consists of several pneumatic units. We put forward a force analysis model of a pneumatic unit and a kinematics model of honeycomb pneumatic network. Based on these models, we study the relationships between the air pressure, external force and geometrical shape through simulation. The experimental results showed that the excellent expansion rate and flexibility can be achieved in the HPN robot.