Data Models Applied to Soft Robot Modeling and Control: A Review

Soft robots show compliance and have infinite degrees of freedom. Thanks to these properties, such robots are leveraged for surgery, rehabilitation, biomimetics, unstructured environment exploring, and industrial gripper. In this case, they attract scholars from a variety of areas. However, nonlinearity and hysteresis effects also bring a burden to robot modeling. Moreover, following their flexibility and adaptation, soft robot control is more challenging than rigid robot control. In order to model and control soft robots, a large number of data models are utilized in pairs or separately. This review classifies these applied data models into five kinds, which are the Jacobian model, analytical model, statistical model, neural network, and reinforcement learning, and compares the modeling and controller features, e.g., model dynamics, data requirement, and target task, within and among these categories. A discussion about the development of the existing modeling and control approaches is presented, and we forecast that the combination of offline-trained and online-learning controllers will be the widespread implementation in the future.

A bistable soft gripper with mechanically embedded sensing and actuation for fast closed-loop grasping

Soft robotic grippers are shown to be high effective for grasping unstructured objects with simple sensing and control strategies. However, they are still limited by their speed, sensing capabilities and actuation mechanism. Hence, their usage have been restricted in highly dynamic grasping tasks. This paper presents a soft robotic gripper with tunable bistable properties for sensor-less dynamic grasping. The bistable mechanism allows us to store arbitrarily large strain energy in the soft system which is then released upon contact. The mechanism also provides flexibility on the type of actuation mechanism as the grasping and sensing phase is completely passive. Theoretical background behind the mechanism is presented with finite element analysis to provide insights into design parameters. Finally, we experimentally demonstrate sensor-less dynamic grasping of an unknown object within 0.02 seconds, including the time to sense and actuate.

Modelling the Nonlinear Response of Fibre-reinforced Bending Fluidic Actuators

Soft actuators are receiving increasing attention from the engineering community, not only in research but even for industrial applications. Among soft actuators, fibre-reinforced Bending Fluidic Actuators (BFAs) became very popular thanks to features such as robustness and easy design and fabrication. However, an accurate modelling of these smart structures, taking into account all the nonlinearities involved, is a challenging task. In this effort, we propose an analytical mechanical model to capture the quasi-static response of fibre-reinforced BFAs. The model is fully 3D and for the first time includes the effect of the pressure on the lateral surface of the chamber as well as the non-constant torque produced by the pressure at the tip. The presented model can be used for design and control, while providing information about the mechanics of these complex actuators.