This article presents a versatile soft robotic gripper system whereby its fingers can be reconfigured into different poses such as scoop, pinch, and claw. This allows the gripper to efficiently and safely handle food samples of different shapes, sizes and stiffness such as uncooked tofu and broccoli floret. The 3D-printed fingers were tested to last up to 25 000 cycles without significant changes in the curvature profile and force output profile. A benchmark experiment was conducted to evaluate the performance of the gripper and state-of-the-art gripping solutions. Capability of versatile soft gripper was optimized by integrating vision and tactile sensing facilities. An object recognition system was developed to identify food samples such as potato, broccoli, and sausage. Position and orientation of food samples were identified and pick-and-place pathway was optimized to achieve the best gripping performance. Flexible tactile sensors were integrated into soft fingers and closed-loop force feedback control system was developed. This allowed the gripper to automatically explore and select the most stable grip pose for different food samples. Integration of vision and force feedback system ensure that objects detected by the system would be firmly gripped. The reconfigurable soft robotic gripper system has been demonstrated to perform high-speed pick-and-place tasks (∼3 s per item) with object recognition system, making it a potential solution to food and grocery supply chain needs.

In the soft gripper design work, most of the designs such as gripping width and the design of finger actuator are purely based on experience, and repeated trial-and-error. In most scenarios, the designed actuators cannot achieve the best/optimized grasping performance with a specific design type. This optimized design is important especially for the food grasping application, as a minor improvement of the grasping capability will be helpful to increase the grasping success ratio, especially during high-speed pick and place tasks. That motivates us to develop a design optimization framework, focusing on how to achieve an optimized grasping performance with a multi-objective design optimization. In this work, a simulation aided data-driven optimization framework for guiding the design of a reconfigurable soft gripper system is presented. To achieve an effective optimization, a simulation model is developed based on the Simulation Open Framework Architecture (SOFA) platform. This model can predict the bending and grasping behavior under actuation and external loading. This model is then used in a data-driven design optimization framework for optimizing the actuator design. An artificial neural network (ANN) is built based on the simulation results as training data, and used as a surrogate model in a multi-objective optimization framework, to achieve an optimal grasping capability with design constraints. This simulation and optimization capability can significantly reduce the trial-and-error design work, and has a great potential for effectively developing soft robots in industrial applications, such as food manufacturing and health care.

The high-pressure vortex tube is one of key equipment in the skid-mounted package liquefied CBM. The high pressure gas strong rotating flow in vortex tube is the key issues of energy separation. There are many problems that the current experimental methods for the strong high-pressure in three-dimensional rotating turbulent flow and heat transfer measurements. The flow field and temperature field structure characteristics in the high-pressure vortex tube are analyzed under different external pressure by numerical simulation model. The design of high-pressure vortex tube hot region should be focused on solving the problem of gas accumulation. This works provide guidance on the development of the high-pressure vortex tube of fluidified CBM.