Modeling and Control of a Soft Robotic Arm Based on a Fractional Order Control Approach

With the emerging sector of new industrial age is afoot, innovations are made every day in order to cope with new technologies. Maintenance and regular repairs are key roles in keeping the industry as efficient as possible. With maintenance, safety is paramount in order to prevent accidents and further delays in production. In order to tackle issues for inspecting hazardous and tight spaces, this study utilizes soft robotics technology in aiming to achieve an efficient and cost-effective means to safely inspect tight and hazardous spaces. Soft Robotics is a field in robotics that specializes in materials that are flexible and elastic. Their movements mimic movements that are often found in nature. The soft robotic arm that will be accomplished in this study is a soft robotic tentacle arm with a mounted camera for inspection. This is especially good in reaching places that have limited spaces. The soft robotic arm will be actuated pneumatically and will have an electronic pneumatic microcontroller for its activation. A minicamera will also be mounted on the tip of the robotic arm for the inspection system. With the results and data gathered, it shows that this system can effectively maneuver using its electronic pneumatic controllers. The soft robot arm is also stable enough for the mini camera to be mounted on.

Soft robots are challenging to model and control due to their poorly defined kinematics and nonlinear dynamics. Recently, Koopman operator theory has been shown capable of constructing control-oriented soft robot models from data. However, building these models requires extensive data collection and they do not necessarily generalize well outside of the training observations. This paper presents a more data-efficient and generalizable approach to soft robot modeling that first identifies a physics-based Koopman model then supplements it with a data-driven residual Koopman model. The resulting combined model is linear and thus compatible with real-time model-based control techniques such as Model Predictive Control (MPC). The efficacy of the approach is demonstrated on several simulated systems and on a real soft robot arm, where it is shown to generate models that are more accurate than purely physics-based models and require less data to construct than purely data-driven models. Using a model-based controller, the soft arm is able to successfully track end effector trajectories, perform a pick-and-place task, and write on a dry-erase board, showcasing the applicability of this framework to increase the capabilities of soft robotic systems.

Inherent compliance equips soft robotic arms with such advantages as incomparable flexibility, good adaptability, and safe interaction with the environment, etc. However, the strong nonlinearity also brings challenges to predict their dynamic behaviors. This work presents a simplified dynamic model of an underwater soft robotic arm which has three fiber-reinforced hydraulic chambers distributed symmetrically in each section. By controlling the pressures in the chambers, the soft robotic arm can perform complex spatial motion with multiple degrees of freedom. The model is based on Lagrangian method and piecewise constant curvature hypothesis, and considers the nonlinear viscoelasticity of soft material. The model accuracy is verified by the experiments of a two-section soft robotic arm with different actuation modes. The effects of geometrical features on dynamic response are also investigated through model-based simulation and test verification, which can provide guidance to parameter optimization. The proposed dynamic model can be extended to multiple sections and contribute to behavior analysis, performance prediction, and motion control of the soft robotic arm.

Manipulating a flexible ureteroscope is difficult, due to its bendable body and hand–eye coordination problems, especially when exploring the lower pole of the kidney. Though robotic interventions have been adopted in various clinical scenarios, they are rarely used in ureteroscopy. This study proposes a teleoperation system consists of a soft robotic endoscope together with a Guidance Virtual Fixture (GVF) to help users explore the kidney’s lower pole. The soft robotic arm was a cable-driven, 3D-printed design with a helicoid structure. GVF was dynamically constructed using video streams from an endoscopic camera. With a haptic controller, GVF can provide haptic feedback to guide the users in following a trajectory. In the user study, participants were asked to follow trajectories when the soft robotic arm was in a retroflex posture. The results suggest that the GVF can reduce errors in the trajectory tracking tasks when the users receive the proper training and gain more experience. Based on the NASA Task Load Index questionnaires, most participants preferred having the GVF when manipulating the robotic arm. In conclusion, the results demonstrate the benefits and potential of using a robotic arm with a GVF. More research is needed to investigate the effectiveness of the GVFs and the robotic endoscope in ureteroscopic procedures.

Because of many advantages such wide bandwidth, high code rate, and low power consumption, the laser space communication has been the new direction of the space communication development, and the APT (acquisition, pointing and tracing) technology is one of key technology for the laser space communication. The ATP rough tracing servo turntable studied in this article adopts two-axes-four-frame structure and the fractional order control strategy is adopted in the rough tracing position loop. The simulation result shows that, comparing with common controller, the fractional order controller could improve the dynamic response characteristics of the system.

This paper investigates the use of fractional order (FO) controllers for a microgrid. The microgrid employs various autonomous generation systems like wind turbine generator, solar photovoltaic, diesel energy generator, and fuel-cells. Other storage devices like the battery energy storage system and the flywheel energy storage system are also present in the power network. An FO control strategy is employed and the FO-proportional integral derivative (PID) controller parameters are tuned with a global optimization algorithm to meet system performance specifications. A kriging based surrogate modeling technique is employed to alleviate the issue of expensive objective function evaluation for the optimization based controller tuning. Numerical simulations are reported to prove the validity of the proposed methods. The results for both the FO and the integer order controllers are compared with standard evolutionary optimization techniques, and the relative merits and demerits of the kriging based surrogate modeling are discussed. This kind of optimization technique is not only limited to this specific case of microgrid control, but also can be ported to other computationally expensive power system optimization problems.

The suckers on the octopus arm play a pivotal role in the execution of tasks in unstructured environments by providing a means to grip objects as well as perceive the environment through (chemo‐)tactile receptors in the suckers. This work presents an octopus‐inspired suction cup with high‐resolution tactile sensing capabilities using a camera that captures the displacement of markers that are integrated in the suction cup. The orientation of the suction cup with respect to an object surface could be predicted with an average error of 1.97° for latitude and 9.41° for longitude. In a closed‐loop control experiment, the orientation of the suction cup with respect to the object surface is estimated by an initial touch and the suction cup is consequently reoriented to approach the object surface in a perpendicular manner. The passive compliance of the suction cup is sufficient to compensate for the prediction error and a seal could be created on all of the objects. In combination with the automated design and manufacturing process, this is a major step toward the deployment of sensory innervated suction cups for motion planning and control of soft continuum robot arms.

Soft robots can exhibit diverse behaviors with simple types of actuation by partially outsourcing control to the morphological and material properties of their soft bodies, which is made possible by the tight coupling between control, body, and environment. In this paper, we present a method that will quantitatively characterize these diverse spatiotemporal dynamics of a soft body based on the information-theoretic approach. In particular, soft bodies have the ability to propagate the effect of actuation through the entire body, with a certain time delay, due to their elasticity. Our goal is to capture this delayed interaction in a quantitative manner based on a measure called momentary information transfer. We extend this measure to soft robotic applications and demonstrate its power using a physical soft robotic platform inspired by the octopus. Our approach is illustrated in two ways. First, we statistically characterize the delayed actuation propagation through the body as a strength of information transfer. Second, we capture this information propagation directly as local information dynamics. As a result, we show that our approach can successfully characterize the spatiotemporal dynamics of the soft robotic platform, explicitly visualizing how information transfers through the entire body with delays. Further extension scenarios of our approach are discussed for soft robotic applications in general.

Although, the compliance of soft robots makes them difficult to control accurately, it also potentially provides an inherently safer solution for coordinated multi-arm manipulation tasks. This is particularly true since deviations in end effector pose do not result in large force buildup in the arms or in the object being manipulated. Coordinated, multi-arm manipulation with soft robots is the goal of the research in this paper. In order to enable this we first develop an evolutionary optimization algorithm to determine the initial placement of each arm for a multi-arm task. We then demonstrate the utility of this optimization in simulation. Lastly, we demonstrate a simple multi-arm control scheme on real soft robots using one soft robot arm, and one rigid robot with low-impedance torque control to manipulate an object through two desired trajectories.

The use of soft robots to interact with soft, fragile, and even living things has spurred interest in the development of soft robotic systems that can deform and adapt themselves to their environment through their inherent compliance. Current soft pneumatic actuator concepts require significant redesign to be repurposed from one function to another, and a complete soft robotic system may be composed of multiple types of actuators with varying materials, designs, and manufacturing methods. This complicates the process of designing and manufacturing soft robots that can tackle new tasks and environments. This work introduces a repeatable pattern of pouch motors with geometric constraints allowing the actuator to produce a programmable bending deformation. By varying the dimensions of the pattern, it is possible to produce different motions that can be used to build diverse robotic components, such as a soft robotic arm or a large soft robotic gripper.

Soft robots actively interact with their environment, exhibiting large distributed deformations due to their structural compliance. These features make them ideal for applications in wearable robotics, medical devices, and assistive technologies. However, a major challenge in their development is integrating sensory systems that do not constrain their movements while enabling accurate estimation of the end-effector position. This work focuses on addressing this limitation in the context of a soft robotic arm. Commercial piezoresistive textile Electrolycra was identified as a suitable candidate for strain sensing. Following electromechanical characterization, Electrolycra sensors were integrated into the soft arm. Using a derived calibration curve and the constant curvature approximation, the system's performance in position tracking was evaluated. The sensing system and estimation method successfully tracked the end-effector's position with errors of 13.1 mm, 13.7 mm, and 4.8 mm in the x, y, and z coordinates, respectively, within a global reference frame during workspace operation. These results demonstrate the potential of Electrolycra-based sensing for soft robotic applications and open avenues for further development, particularly in closed-loop control systems.Clinical Relevance- A sensorized soft robot with enhanced closed-loop control can significantly impact the clinical field by enabling precise and adaptive interactions with patients, improving safety, efficacy, and personalization in rehabilitation, assistive technologies, and motor assessment.

The behaviors of the animals or embodied agents are characterized by the dynamic coupling between the brain, the body, and the environment. This implies that control, which is conventionally thought to be handled by the brain or a controller, can partially be outsourced to the physical body and the interaction with the environment. This idea has been demonstrated in a number of recently constructed robots, in particular from the field of “soft robotics”. Soft robots are made of a soft material introducing high-dimensionality, non-linearity, and elasticity, which often makes the robots difficult to control. Biological systems such as the octopus are mastering their complex bodies in highly sophisticated manners by capitalizing on their body dynamics. We will demonstrate that the structure of the octopus arm cannot only be exploited for generating behavior but also, in a sense, as a computational resource. By using a soft robotic arm inspired by the octopus we show in a number of experiments how control is partially incorporated into the physical arm's dynamics and how the arm's dynamics can be exploited to approximate non-linear dynamical systems and embed non-linear limit cycles. Future application scenarios as well as the implications of the results for the octopus biology are also discussed.

Kinematic modeling and control of a robot arm using unit dual quaternions

<p>Soft robots have become important members of the robot community with many potential applications owing to their unique flexibility and security embedded at the material level. An increasing number of researchers are interested in their designing, manufacturing, modeling, and control. However, the dynamic simulation of soft robots is difficult owing to their infinite degrees of freedom and nonlinear characteristics that are associated with soft materials and flexible geometric structures. In this study, a novel multi-flexible body dynamic modeling and simulation technique is introduced for soft robots. Various actuators for soft robots are modeled in a virtual environment, including soft cable-driven, spring actuation, and pneumatic driving. A pneumatic driving simulation was demonstrated by the bending modules with different materials. A cable-driven soft robot arm prototype and a cylindrical soft module actuated by shape memory alley springs inspired by an octopus were manufactured and used to validate the simulation model, and the experimental results demonstrated adequate accuracy. The proposed technique can be widely applied for the modeling and dynamic simulation of other soft robots, including hybrid actuated robots and rigid-flexible coupling robots. This study also provides a fundamental framework for simulating soft mobile robots and soft manipulators in contact with the environment.</p> <p> </p>

Planar soft space robotic manipulators: Dynamic modeling and control