Human-Powered Master Controllers for Reconfigurable Fluidic Soft Robots.

Soft continuum robots are more and more used in various applications due to the multiple advantages offered by the properties of hyper-elastic soft materials, such as resilience, flexibility, etc. Despite all these essential properties that make the soft robots good candidates for some real-life needs (form enclosure grasping, obstacle-free navigation, etc.), controlling their shape remains challenging because of their hyper-redundancies. This letter investigates an inverse dynamics model-based shape control of soft continuum robots in the presence and absence of external efforts. To that end, modeling based on Pythagorean Hodograph (PH) curves with prescribed lengths is combined with Euler-Bernoulli modeling to reconstruct the robot shapes and then calculate the actuator inputs in the case of external efforts. Thus, a relationship between the actuator inputs and the PH control points is obtained. With the above strategy, the actuator inputs can be computed accordingly, and therefore, real-time shape control of the soft robots for various tasks becomes possible. The results of the proposed approach are validated both numerically and experimentally using two classes of soft continuum robots - a Fluidic Elastomeric Actuator (FEA), describing a 2D soft finger robot, with a single and multiple phalanges, and a 3D continuum manipulator, namely the Compact Bionic Handling Assistant (CBHA).

Soft robots have recently drawn extensive attention thanks to their unique ability of adapting to complicated environments. Soft robots are designed in a variety of shapes of aiming for many different applications. However, accurate modelling and control of soft robots is still an open problem due to the complex robot structure and uncertain interaction with the environment. In fact, there is no unified framework for the modeling and control of generic soft robots. In this paper, we present a novel data-driven machine learning method for modeling a cable-driven soft robot. This machine learning algorithm, named deterministic learning (DL), uses soft robot motion data to train a radial basis function neural network (RBFNN). The soft robot motion dynamics are then guaranteed to be accurately identified, represented, and stored as an RBFNN model with converged constant neural network weights. To validate our method, We have built a simulated soft robot almost identical to our real inchworm soft robot, and we have tested the DL algorithm in simulation. Furthermore, a neural network weight combining technique is used which can extract and combine useful dynamics information from multiple robot motion trajectories.

Energy in Robotics: An Interdisciplinary Challenge

Soft robots have been attracting much research attention in the past decades owing to their unique properties that are inherently different from conventional rigid robots. The intrinsic characteristics of soft robots, such as compliance and flexibility, have made them a promising supplement for many existing robotic applications. For instance, soft robots provide safer human-machine interaction and better adaptability to unexplored environments than the conventional rigid ones. However, their “soft” feature would conversely cause complex material deformation due to their high-DoF nature, which has made their kinematic or dynamic models more difficult to deduce. Logically, the process of designing model-based controllers would therefore suffer from the lack of efficient and accurate modeling methods. In this letter, through adopting Finite Element Method (FEM), we demonstrate a FEM-based nonlinear control method for a soft trunk robot to achieve trajectory tracking objectives. The proposed controller is developed by utilizing the essential information from the dynamic FE model of the studied robot. The details of deriving this nonlinear control method are demonstrated, then followed by 4 practical experiments on the soft truck robot to verify the feasibility of the control method. The corresponding experimental results are presented accordingly.

This research investigates designing a continuum soft robot and proposing a kinematic matching control to enable the robot to perform a specified medical task, which in this paper is the transesophageal echocardiography (TEE). A multi-chamber soft robot was designed and fabricated based on the molding of separate layers. The method of transformation matrices was used to develop the kinematic models, and a control method using Jacobian matrices was proposed to manipulate the robot. A prototype was made based on a multi-chamber multi-layer design. The system contains three segments that can be actuated independently to mimic the active bending part of the respective probe. Kinematic models were developed. Negative pressure (vacuum) was used as actuation input. An open-loop controller inspired by a redundancy resolution technique was proposed to make the soft robot tip follow the desired path, i.e.the path of the rigid ultrasound probe. It is concluded that the soft solution can perform the required task as the reachable points of the TEEtip cover the proposed robot workspace and the proposed control can be used for maneuvering in arbitrary trajectories.

Soft robots are compliant and durable, and thus potentially better-suited for swimming or crawling over rough terrain than conventional rigid robots. Biomimetic locomotion strategies are often implemented on soft robots, but are difficult to tune because of the complexity of mapping nonlinear mechanical systems to specific gait patterns. We propose an approach to soft robot control in which the robot is designed to physically realize a shape space (mechanically predefined body poses) to follow a theoretically optimal gait. Specifically in this paper we present an entirely soft snake robot designed to implement the prerequisite shape space for slithering gaits. When coupled with a geometric mechanics model relating serpentine shape sequences to displacement, appropriate gait patterns can be selected analytically based on the system's Lie bracket. We developed a modular, two-link soft robot and used the geometric mechanics model to identify several cyclic gait patterns for producing forward displacement. The gait patterns were tested on the soft robot and observed with a motion capture system to measure displacement and monitor shape sequences. Relative performance of the gait patterns is consistent with the geometric “soap-bubble method” as a heuristic for gait optimization, which demonstrates the applicability of this approach to soft robot control and coordination.

Soft robots are primarily composed of soft materials that can allow for mechanically robust maneuvers that are not typically possible with conventional rigid robotic systems. However, owing to the current limitations in simulation, design and control of soft robots often involve a painstaking trial. With the ultimate goal of a computational framework for soft robotic engineering, here we introduce a numerical simulation tool for limbed soft robots that draws inspiration from discrete differential geometry based simulation of slender structures. The simulation incorporates an implicit treatment of the elasticity of the limbs, inelastic collision between a soft body and rigid surface, and unilateral contact and Coulombic friction with an uneven surface. The computational efficiency of the numerical method enables it to run faster than real-time on a desktop processor. Our experiments and simulations show quantitative agreement and indicate the potential role of predictive simulations for soft robot design.

Soft robotics has recently evolved to cover a wide range of fields and applications, repeatably proving to be more valuable than previous iterations of robotics. One application is the implementation of soft continuum robots for medical applications as the form and flexibility of soft robots allow them to navigate through vessels in the human body. However, control of these soft bodied robots is difficult. We present a general control strategy for the complex kinematic control of a soft continuum robot based on learning the forward kinematics. Our results show that implementing a kinematic controller using learned forward kinematics is accurate.

Assistive behavior and inherent compliance are assumed to be the essential properties for effective robot-assisted therapy in neurological as well as in orthopedic rehabilitation. This paper presents two adaptive model-based assistive controllers for pneumatic direct driven soft rehabilitation robots that are based on separated models of the soft-robot and the patient's extremity, in order to take into account the individual patient's behavior, effort and ability during control, what is assumed to be essential to relearn lost motor functions in neurological and facilitate muscle reconstruction in orthopedic rehabilitation. The high inherent compliance of soft-actuators allows for a general human-robot interaction and provides the base for effective and dependable assistive control. An inverse model of the soft-robot with estimated parameters is used to achieve robot transparency during treatment and inverse adaptive models of the individual patient's extremity allow the controllers to learn on-line the individual patient's behavior and effort and react in a way that assist the patient only as much as needed. The effectiveness of the controllers is evaluated with unimpaired subjects using a first prototype of a soft-robot for elbow training. Advantages and disadvantages of both controllers are analyzed and discussed.

A systematic trend analysis of 3D printing techniques used in specific soft robotic elements

Fluidic soft actuators have been widely used for applications where compliance is desirable – such as in delicate robotic manipulation. As their operation is based on pressurization, fluidic actuators typically rely on bulky, rigid pumps, valves, and pressure regulators for control. This dependence on rigid components hinders the development of compact and fully-soft robots. Soft regulation systems designed for control of these actuators have been recently developed based on soft valves using dielectric elastomer actuators, but precise control has not been achieved. In this work, we leverage these valves to introduce a soft regulation system capable of precise closed-loop position control of a soft hydraulic actuator with multiple controllers. We also achieve open-loop trajectory tracking based on a data-driven model of the fluidic system. Finally, we combine the valve system with wearable strain sensors to create the first teleoperated fluidic circuit where the sensor, actuator, and regulation system are all soft. This work presents control strategies for fluid-driven actuators with soft sensors and regulation systems, showing the potential for future all-soft motion control of soft hydraulic robots.

Despite an ever increasing variety of soft sensors and actuators, there are few entirely soft robots that integrate both modalities into an autonomous system. Often, a minimum set of rigid components such as microcontrollers, are embedded into soft systems, compromising their flexibility. This has led to growing interest in entirely compliant versions of controllers for soft robots. In this work, we present the first integration of two such soft control systems: the soft, bistable valve and the soft matter computer. By combining them we allow information to pass from the pneumatic to the electrical domain, enabling control of electric soft actuators with pneumatic control signals. Our work is an important step towards the development of fully compliant robots and machines.

Soft robotics holds enormous promise for a wide class of applications. However, system controllability, bandwidth, portability, and energy efficiency of soft robot power supplies are often inadequate. Soft robotics desperately needs improved solutions to drive soft actuators either pneumatically or hydraulically. This research paper offers a contribution to bridge this gap. It deals with small-scale power supplies for hydraulically-driven soft robots based on fluidic elastomer actuators in the power range 5-400 W. A design procedure for such power supplies is developed with an emphasis on high-bandwidth control. The performance requirements are established based on a literature survey, and design alternatives are discussed. Then, a prototype consisting of a $0.102 c^{3}$/rev positive-displacement, external gear pump directly driven by a 70 W brushless dc motor is built and experimentally characterized. Control of the targeted soft robots results possible because the proposed power supply has high-bandwidth varying between about 12 and 30 Hz (i.e., well above the actuators’ mechanical frequency). The power supply’s energy efficiency is also measured, and areas for its improvement are identified. The proposed solution is feasible, applicable to autonomous soft robots, scalable to the power levels of interest, and possibly useful for both tethered soft robots and rigid/semi-rigid robots.

Simulating soft robots offers a cost-effective approach to exploring their design and control strategies. While current models, such as finite element analysis, are effective in capturing soft robotic dynamics, the field still requires a broadly applicable and efficient numerical simulation method. In this letter, we introduce a discrete differential geometry-based framework for the model-based inverse design of a novel snap-actuated jumping robot. Our findings reveal that the snapping beam actuator exhibits both symmetric and asymmetric dynamic modes, enabling tunable robot trajectories (e.g., horizontal or vertical jumps). Leveraging this bistable beam as a robotic actuator, we propose a physics-data hybrid inverse design strategy to endow the snap-jump robot with a diverse range of jumping capabilities. By utilizing a physical engine to examine the effects of design parameters on jump dynamics, we then use extensive simulation data to establish a data-driven inverse design solution. This approach allows rapid exploration of parameter spaces to achieve targeted jump trajectories, providing a robust foundation for the robot's fabrication. Our methodology offers a powerful framework for advancing the design and control of soft robots through integrated simulation and data-driven techniques.

This article introduces a model-based robust control framework for electrohydraulic soft robots. The methods presented herein exploit linear system control theory as it applies to a nonlinear soft robotic system. We employ dynamic mode decomposition with control (DMDc) to create appropriate linear models from real-world measurements. We build on the theory by developing linear models in various operational regions of the system to result in a collection of linear plants used in uncertainty analysis. To complement the uncertainty analyses, we utilize ("H Infinity") synthesis techniques to determine an optimal controller to meet performance requirements for the nominal plant. Following this methodology, we demonstrate robust control over a multi-input multi-output (MIMO) hydraulically amplified self-healing electrostatic (HASEL)-actuated system. The simplifications in the proposed framework help address the inherent uncertainties and complexities of compliant robots, providing a flexible approach for real-time control of soft robotic systems in real-world applications.