Soft actuators, as an important part of soft robotics, have attracted significant attention due to their inherent compliance, flexibility and safety. However, low capacity in force and load limits their applications. Prestored elastic energy can improve the capacity in output force and load of soft actuators. This work introduces a soft pneumatic bistable reinforced actuator inspired by the Venus Flytrap's bistable mechanism that allows for the storage of elastic energy. The proposed actuator consists of two separated 3D-printed actuation chambers that are attached to a central prestressed steel shell to achieve bistability. The pressure triggering the pneumatic bistable reinforced actuator from one stable state to the other is derived and validated by experiments. Further experimental comparisons between the proposed actuator and a dual chamber actuator show that the proposed design significantly improves the block tip force, load capacity and stiffness. The pneumatic bistable reinforced actuator also demonstrates superior performance in the actuation speed and bending angle under the same input pressure. Finally, a two-finger gripper is developed using the proposed actuator, which is demonstrated to grasp and hold various objects.

Soft bending actuators, as one of the most important components of soft robotics, have attracted significantly increasing attention due to their robustness, compliance, inherent safety, and ease of manufacture. However, the key disadvantages can be the low output force, slow response speed, large deformation and vibration, which can potentially be addressed by introducing a bistable mechanism enabled by a prestressed steel shell. This work proposes a novel soft actuator with bistable property, which can maintain the predefined initial state and enhance bending motion at the corresponding stable state. A novel dual-actuation mechanism, which utilises pneumatic pressure for closing and tendon-driven for opening process, is proposed for autonomous transition between both states, and for a fast response. Mathematical model is proposed and compared with the experimental result for triggering pressure, which serves as a threshold to activate the transition of the stable state. Experimental results also indicate that closing and opening speeds are enhanced by 9.82% and more than ten times, respectively, as compared with the existing pneumatic bistable reinforced actuator design. Mathematical and experimental results suggest that a programmable bending angle at the second stable state can also be achieved by adjusting the preset tendon extension. The tendon arrangement also acts as a passive damping mechanism to reduce the oscillation while closing. The damping ratio is increased by more than four times, indicating that the oscillation decay is significantly accelerated for quick stabilization. Finally, a three-finger soft gripper is developed based on the proposed actuator design, which demonstrates promising performance in grasping objects with various shapes and sizes. The experimental results also show that the proposed bistable gripper can grasp the object with a weight up to 2067 g, which is more than 17 times heavier than that of three actuators.

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Robotic harvesting research has seen significant achievements in the past decade, with breakthroughs being made in machine vision, robot manipulation, autonomous navigation and mapping. However, the missing capability of obstacle handling during the grasping process has severely reduced harvest success rate and limited the overall performance of robotic harvesting. This work focuses on leaf interference caused slip detection and handling, where solutions to robotic grasping in an unstructured environment are proposed. Through analysis of the motion and force of fruit grasping under leaf interference, the connection between object slip caused by leaf interference and inadequate harvest performance is identified for the first time in the literature. A learning-based perception and manipulation method is proposed to detect slip that causes problematic grasps of objects, allowing the robot to implement timely reaction. Our results indicate that the proposed algorithm detects grasp slip with an accuracy of 94%. The proposed sensing-based manipulation demonstrated great potential in robotic fruit harvesting, and could be extended to other pick-place applications.

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