Nonparametric Online Learning Control for Soft Continuum Robot: An Enabling Technique for Effective Endoscopic Navigation.

Bioinspired robotic structures comprising soft actuation units have attracted increasing research interest. Taking advantage of its inherent compliance, soft robots can assure safe interaction with external environments, provided that precise and effective manipulation could be achieved. Endoscopy is a typical application. However, previous model-based control approaches often require simplified geometric assumptions on the soft manipulator, but which could be very inaccurate in the presence of unmodeled external interaction forces. In this study, we propose a generic control framework based on nonparametric and online, as well as local, training to learn the inverse model directly, without prior knowledge of the robot's structural parameters. Detailed experimental evaluation was conducted on a soft robot prototype with control redundancy, performing trajectory tracking in dynamically constrained environments. Advanced element formulation of finite element analysis is employed to initialize the control policy, hence eliminating the need for random exploration in the robot's workspace. The proposed control framework enabled a soft fluid-driven continuum robot to follow a 3D trajectory precisely, even under dynamic external disturbance. Such enhanced control accuracy and adaptability would facilitate effective endoscopic navigation in complex and changing environments

Learning by imitation with the STIFF-FLOP surgical robot: a biomimetic approach inspired by octopus movements

Transferring skills from a biological organism to a hyper-redundant system is a challenging task, especially when the two agents have very different structure/embodiment and evolve in different environments. In this article, we propose to address this problem by designing motion primitives in the form of probabilistic dynamical systems. We take inspiration from invertebrate systems in nature to seek for versatile representations of motion/behavior primitives in continuum robots. We take the perspective that the incredibly varied skills achieved by the octopus can guide roboticists toward the design of robust motor skill encoding schemes and present our ongoing work that aims at combining statistical machine learning, dynamical systems, and stochastic optimization to study the problem of transferring movement patterns from an octopus arm to a flexible surgical robot (STIFF-FLOP) composed of two modules with constant curvatures. The approach is tested in simulation by imitation and self-refinement of an octopus reaching motion

Learning-Based Control Strategies for Soft Robots: Theory, Achievements, and Future Challenges

In the last few decades, soft robotics technologies have challenged conventional approaches by introducing new, compliant bodies to the world of rigid robots. These technologies and systems may enable a wide range of applications, including human-robot interaction and dealing with complex environments. Soft bodies can adapt their shape to contact surfaces, distribute stress over a larger area, and increase the contact surface area, thus reducing impact forces

Simultaneous Position-and-Stiffness Control of Underactuated Antagonistic Tendon-Driven Continuum Robots

Continuum robots have gained widespread popularity due to their inherent compliance and flexibility, particularly their adjustable levels of stiffness for various application scenarios. Despite efforts to dynamic modeling and control synthesis over the past decade, few studies have focused on incorporating stiffness regulation in their feedback control design; however, this is one of the initial motivations to develop continuum robots. This paper aims to address the crucial challenge of controlling both the position and stiffness of a class of highly underactuated continuum robots that are actuated by antagonistic tendons. To this end, the first step involves presenting a high-dimensional rigid-link dynamical model that can analyze the open-loop stiffening of tendon-driven continuum robots. Based on this model, we propose a novel passivity-based position-and-stiffness controller adheres to the non-negative tension constraint. To demonstrate the effectiveness of our approach, we tested the theoretical results on our continuum robot, and the experimental results show the efficacy and precise performance of the proposed methodology

Modeling, simulation, and control of soft robots

2019 Fall.Includes bibliographical references.Soft robots are a new type of robot with deformable bodies and muscle-like actuations, which are fundamentally different from traditional robots with rigid links and motor-based actuators. Owing to their elasticity, soft robots outperform rigid ones in safety, maneuverability, and adaptability. With their advantages, many soft robots have been developed for manipulation and locomotion in recent years. However, the current state of soft robotics has significant design and development work, but lags behind in modeling and control due to the complex dynamic behavior of the soft bodies. This complexity prevents a unified dynamics model that captures the dynamic behavior, computationally-efficient algorithms to simulate the dynamics in real-time, and closed-loop control algorithms to accomplish desired dynamic responses. In this thesis, we address the three problems of modeling, simulation, and control of soft robots. For the modeling, we establish a general modeling framework for the dynamics by integrating Cosserat theory with Hamilton's principle. Such a framework can accommodate different actuation methods (e.g., pneumatic, cable-driven, artificial muscles, etc.). To simulate the proposed models, we develop efficient numerical algorithms and implement them in C++ to simulate the dynamics of soft robots in real-time. These algorithms consider qualities of the dynamics that are typically neglected (e.g., numerical damping, group structure). Using the developed numerical algorithms, we investigate the control of soft robots with the goal of achieving real-time and closed-loop control policies. Several control approaches are tested (e.g., model predictive control, reinforcement learning) for a few key tasks: reaching various points in a soft manipulator's workspace and tracking a given trajectory. The results show that model predictive control is possible but is computationally demanding, while reinforcement learning techniques are more computationally effective but require a substantial number of training samples. The modeling, simulation, and control framework developed in this thesis will lay a solid foundation to unleash the potential of soft robots for various applications, such as manipulation and locomotion

Robotic manipulators for in situ inspections of jet engines

Jet engines need to be inspected periodically and, in some instances, repaired. Currently, some of these maintenance operations require the engine to be removed from the wing and dismantled, which has a significant associated cost. The capability of performing some of these inspections and repairs while the engine is on-wing could lead to important cost savings. However, existing technology for on-wing operations is limited, and does not suffice to satisfy some of the needs. In this work, the problem of performing on-wing operations such as inspection and repair is analysed, and after an extensive literature review, a novel robotic system for the on-wing insertion and deployment of probes or other tools is proposed. The system consists of a fine-positioner, which is a miniature and dexterous robotic manipulator; a gross-positioner, which is a device to insert the fine-positioner to the engine region of interest; an end-effector, such as a probe; a deployment mechanism, which is a passive device to ensure correct contact between probe and component; and a feedback system that provides information about the robot state for control. The research and development work conducted to address the main challenges to create this robotic system is presented in this thesis. The work is focussed on the fine-positioner, as it is the most relevant and complex part of the system. After a literature review of relevant work, and as part of the exploration of potential robot concepts for the system, the kinematic capabilities of concentric tube robots (CTRs) are first investigated. The complete set of stable trajectories that can be traced in follow-the-leader motion is discovered. A case study involving simulations and an experiment is then presented to showcase and verify the work. The research findings indicate that CTRs are not suitable for the fine-positioner. However, they show that CTRs with non-annular cross section can be used for the gross-positioner. In addition, the new trajectories discovered show promise in minimally invasive surgery (MIS). Soft robotic manipulators with fluidic actuation are then selected as the most suitable concept for the fine-positioner. The design of soft robotic manipulators with fluidic actuation is investigated from a general perspective. A general framework for the design of these devices is proposed, and a set of design principles are derived. These principles are first applied in a MIS case study to illustrate and verify the work. Finite element (FE) simulations are then reported to perform design optimisation, and thus complete the case study. The design study is then applied to determine the most suitable design for the fine-positioner. An additional analytical derivation is developed, followed by FE simulations, which extend those of the case study. Eventually, this work yields a final design of the fine-positioner. The final design found is different from existing ones, and is shown to provide an important performance improvement with respect to existing soft robots in terms of wrenches it can support. The control of soft and continuum robots relevant to the fine-positioner is also studied. The full kinematics of continuum robots with constant curvature bending and extending capabilities are first investigated, which correspond to a preliminary design concept conceived for the fine-positioner. Closed-form solutions are derived, closing an open problem. These kinematics, however, do not exactly match the final fine-positioner design selected. Thus, an alternative control approach based on closed-loop control laws is then adopted. For this, a mechanical model is first developed. Closed-loop control laws are then derived based on this mechanical model for planar operation of a segment of the fine-positioner. The control laws obtained represent the foundation for the subsequent development of control laws for a full fine-positioner operating in 3D. Furthermore, work on path planning for nonholonomic systems is also reported, and a new algorithm is presented, which can be applied for the insertion of the overall robotic system. Solutions to the other parts of the robotic system for on-wing operations are also reported. A gross-positioner consisting of a non-annular CTR is proposed. Solutions for a deployment mechanism are also presented. Potential feedback systems are outlined. In addition, methods for the fabrication of the systems are reported, and the electronics and systems required for the assembly of the different parts are described. Finally, the use of the robotic system to perform on-wing inspections in a representative case study is studied to determine the viability. Inspection strategies are shortlisted, and simulations and experiments are used to study them. The results, however, indicate that inspection is not viable since the signal to noise ratio is excessively low. Nonetheless, the robotic system proposed, and the research conducted, are still expected to be useful to perform a range of on-wing operations that require the insertion and deployment of a probe or other end-effector. In addition, the trajectories discovered for CTRs, the design found for the fine-positioner, and the advances on control, also have significant potential in MIS, where there is an important need for miniature robotic manipulators and similar devices.Open Acces

SUAS: A Novel Soft Underwater Artificial Skin with Capacitive Transducers and Hyperelastic Membrane

The paper presents physical modeling, design, simulations, and experimentation on a novel Soft Underwater Artificial Skin (SUAS) used as tactile sensor. The SUAS functions as an electrostatic capacitive sensor, and it is composed of a hyperelastic membrane used as external cover and oil inside it used to compensate the marine pressure. Simulation has been performed studying and modeling the behavior of the external interface of the SUAS in contact with external concentrated loads in marine environment. Experiments on the external and internal components of the SUAS have been done using two different conductive layers in oil. A first prototype has been realized using a 3D printer. The results of the paper underline how the soft materials permit better adhesion of the conductive layer to the transducers of the SUAS obtaining higher capacitance. The results here presented confirmed the first hypotheses presented in a last work and opened new ways in the large-scale underwater tactile sensor design and development. The investigations are performed in collaboration with a national Italian project named MARIS, regarding the possible extension to the underwater field of the technologies developed within the European project ROBOSKIN

Deep Reinforcement Learning for Inverse Kinematics and Path Following for Concentric Tube Robots

Concentric tube robots (CTRs) are continuum robots that allow for bending and twisting motions unattainable by traditional rigid link robots. The curvilinear backbones can benefit surgical applications by improving dexterity, enlarging the workspace, and reducing trauma at the entry point of the instrument. The curvilinear backbone that is attributed is the result of pre-curved, super-elastic tubes arranged concentrically. Each tube has a straight and pre-curved section and is actuated in rotation and translation from the tube base with the neighboring tube interactions producing the curvilinear backbone. The modeling of the neighboring tube interactions is non-trivial, and an explored topic in CTR literature. However, model-based kinematics and control can be inaccurate due to inherent manufacturing errors of the tubes, permanent deformation over time, and unmodelled physical interactions. This thesis proposes a model-free control method using deep reinforcement learning (DRL). The DRL framework aims to control the end-effector of the CTR with limited modeling information by leveraging simulation data, which is much less costly than hardware data. To develop a DRL framework, a Markov Decision Process (MDP) with states, actions, and rewards needs to be defined for the inverse kinematics task. First, action exploration was investigated with this MDP in a simpler simulation as CTRs have a unique extension degree of freedom per tube. Next, state representation, curriculum reward, and adaptation methods over multiple CTR systems were developed in a more accurate simulation. To validate the work in simulation, a noise-induced simulation environment was utilized to demonstrate the initial robustness of the learned policy. Finally, a hardware system was developed where a workspace characterization was performed to determine simulation to hardware differences. By using Sim2Real domain transfer, a simulation policy was successfully transferred to hardware for inverse kinematics and path following, validating the approach

Soft pneumatic actuators: a review of design, fabrication, modeling, sensing, control and applications

Soft robotics is a rapidly evolving field where robots are fabricated using highly deformable materials and usually follow a bioinspired design. Their high dexterity and safety make them ideal for applications such as gripping, locomotion, and biomedical devices, where the environment is highly dynamic and sensitive to physical interaction. Pneumatic actuation remains the dominant technology in soft robotics due to its low cost and mass, fast response time, and easy implementation. Given the significant number of publications in soft robotics over recent years, newcomers and even established researchers may have difficulty assessing the state of the art. To address this issue, this article summarizes the development of soft pneumatic actuators and robots up until the date of publication. The scope of this article includes the design, modeling, fabrication, actuation, characterization, sensing, control, and applications of soft robotic devices. In addition to a historical overview, there is a special emphasis on recent advances such as novel designs, differential simulators, analytical and numerical modeling methods, topology optimization, data-driven modeling and control methods, hardware control boards, and nonlinear estimation and control techniques. Finally, the capabilities and limitations of soft pneumatic actuators and robots are discussed and directions for future research are identified

Design, Development and Force Control of a Tendon-driven Steerable Catheter with a Learning-based Approach

In this research, a learning-based force control schema for tendon-driven steerable catheters with the application in robot-assisted tissue ablation procedures was proposed and validated. To this end, initially a displacement-based model for estimating the contact force between the catheter and tissue was developed. Afterward, a tendon-driven catheter was designed and developed. Next, a software-hardware-integrated robotic system for controlling and monitoring the pose of the catheter was designed and developed. Also, a force control schema was developed based on the developed contact force model as a priori knowledge. Furthermore, the position control of the tip of the catheter was performed using a learning-based inverse kinematic approach. By combining the position control and the contact model, the force control schema was developed and validated. Validation studies were performed on phantom tissue as well as excised porcine tissue. Results of the validation studies showed that the proposed displacement-based model was 91.5% accurate in contact force prediction. Also, the system was capable of following a set of desired trajectories with an average root-mean-square error of less than 5%. Further validation studies revealed that the system could fairly generate desired static and dynamic force profiles on the phantom tissue. In summary, the proposed force control system did not necessitate the utilization of force sensors and could fairly contribute in automatizing the ablation task for robotic tissue ablation procedures