An Investigation on the Grasping Position Optimization-Based Control for Industrial Soft Robot Manipulator

Soft manipulators have desirable environmental compatibility because of their pliability. However, this pliability also brings challenges to modeling and control when considering contact or collision with the environment. In previous work, we established several mathematical models for describing fluidic soft manipulators under environmental effects and verified their accuracy. However, the controller design for a soft manipulator is still a significant challenge, especially under the conditions of environmental contact. In this paper, we build upon our previously established work by conducting feedforward compensation for a soft manipulator under contact constraints and designing a sliding mode controller based on an operational space dynamics model. Then, we combine the feedforward compensation model with the sliding mode controller to realize accurate position control of the soft manipulator. Finally, simulation and experimental results show that this controller can accurately and effectively control the position of the soft manipulator.

In this paper, the design and testing of a novel valve for the intuitive spatial control of soft or continuum manipulators are presented. The design of the valve is based on the style of a hydraulic flapper valve, but with simultaneous control of three pressure feed points, which can be used to drive three antagonistically arranged hydraulic actuators for positioning soft robots. The variable control orifices are arranged in a rotationally symmetric radial pattern to allow for an inline mounting configuration of the valve within the body of a manipulator. Positioning the valve ring at various 3D configurations results in different pressurizations of the actuators and corresponding spatial configurations of the manipulator. The design of the valve is suitable for miniaturization and use in applications with size constraints such as small soft manipulators and surgical robotics. Experimental validation showed that the performance of the valve can be reasonably modeled and can effectively drive an antagonistic arrangement of three actuators for soft manipulator control.

Dynamic control of soft robotic manipulators is a challenging field still in its nascent stages. Modeling is still a major hurdle due to its high dimensional nonlinear dynamic properties. Even if accurate models of these high dimensional nonlinear systems are available, the computational burden of the models or sensory requirements poses problems for robust and stable control. This letter addresses these two problems by using a data-driven model that uses only mechanical feedback for stable control of the soft robotic manipulator. Using a learning-based open loop dynamic controller, we investigate the self-stabilizing behavior that can be obtained from the complex dynamics of a soft manipulator. Experimental findings illustrate the advantage of using open-loop dynamic controllers for accurate self-stabilizing control of soft robotic manipulators without any sensory feedback.

Soft robots are concerned by researchers due to several characteristics, such as high adaptability in complex unstructured environments, safe interaction with environments, and a high degree of dexterity. The deformability and dexterity of the octopus arm are remarkable and interesting for the development of bioinspiration soft robots. In this study, we proposed a biomimetic flexible manipulator actuated by shape memory alloy (SMA) wires and its PI controller, and evaluated its performances with focused experiments. This study aims to find out the advantages of the developed manipulator actuated by SMA wires. We designed the bionic structure to achieve flexible bending in 3D space based on the analysis of the octopus muscle structure. Then we built the mathematical models that described the relationship between the bending angle and the driving parameters. Surprisingly, the soft movement capacity of the bionic manipulator was investigated at different heating voltages and PWM duty cycles by the PI controller based on the self-sensing resistance feedback. The soft manipulator was able to bend flexibly with the maximum bending angle of 60°. Compared to the traditional soft arm, the soft manipulator has higher accuracy, with the error of the deflection angle less than 5° and the errors of bending angles less than 2°.

Soft manipulators have been a rising focus of soft robotics research. Taking advantage of soft materials and flexible, continuous movements, they have promising applicable prospect. However, their highly internal nonlinearity and unpredictable deformation caused by environmental effects make it difficult to build an exact model for control. In this work, we propose a generalized controller for soft manipulators using an estimated Jacobian-based model derived from structural analysis. The model can be simplified from reasonable assumptions of manipulator structure, and updated to balance conformity to reality and stability. In prototype experiments on an 3D multi-segment soft manipulator, the control method exhibits accuracy as well as adaptability to self gravity and external loads.

In this paper, the results of several experiments made on the control of oscillations of a single module of the I-Support manipulator are presented. The I-Support is a soft continuum robotic manipulator composed of several similar modules, designed and built for assisting disabled users during a showering task. For these experiments, a single module is pneumatically actuated by three sets of bellow-like actuators, that allow controlled bending in any direction and also controlled extension. The actuators are made of latex and braided polyester components that, when pressurized, make up the load-carrying structure of the module. As motion control of even a single module is a complex non-linear problem, a stepwise approach was followed where several signal based control techniques were tested for feasibility. These techniques focus on the oscillation control itself, meaning that it is assumed that the inverse kinematics problem—the definition of the input signals that will allow the robot to reach a specified equilibrium position—is dealt with elsewhere. Results show that pre-filtering the input commands significantly reduces overshoot and settling time for a task in three-dimensional space. Also, closed-loop control based on feeding back the error from the equilibrium position showed promising results but has for now only been applied to one-dimensional problems.KeywordsFlexible robotsSoft robotsVibration controlFlexible structures

This paper mainly introduces the modeling based on Cosserat rod theory and focuses on the adaptive neural network controller design based on model. In dealing with the external interference with the environment and the unmodeled dynamics of the system, a neural network (NN) is introduced to compensate it, and using Backstepping method to design the adaptive controller, finally the stability of the closed-loop system and the convergence of the signal in the system is proved with Lyapunov function theory, ensuring the end of the manipulator can track the given signal. In addition, the simulation experiment of soft manipulator swing and constant angle tracking control are carried out, and the simulation shows the rationality of the proposed controller.

This paper presents a robotic manipulation system capable of autonomously positioning a multi-segment soft fluidic elastomer robot in three dimensions. Specifically, we present an extremely soft robotic manipulator morphology that is composed entirely from low durometer elastomer, powered by pressurized air, and designed to be both modular and durable. To understand the deformation of a single arm segment, we develop and experimentally validate a static deformation model. Then, to kinematically model the multi-segment manipulator, we use a piece-wise constant curvature assumption consistent with more traditional continuum manipulators. In addition, we define a complete fabrication process for this new manipulator and use this process to make multiple functional prototypes. In order to power the robot’s spatial actuation, a high capacity fluidic drive cylinder array is implemented, providing continuously variable, closed-circuit gas delivery. Next, using real-time data from a vision system, we develop a processing and control algorithm that generates realizable kinematic curvature trajectories and controls the manipulator’s configuration along these trajectories. Lastly, we experimentally demonstrate new capabilities offered by this soft fluidic elastomer manipulation system such as entering and advancing through confined three-dimensional environments as well as conforming to goal shape-configurations within a sagittal plane under closed-loop control.

Fast inverse kinematics (IK) algorithm is significant for real-time precise motion control for continuum and soft manipulators. In this paper, we studied an explicit expression of a two-segment continuum manipulator based on constant curvature assumptions and discussed the existence of the IK solutions. Utilizing the pseudo-rigid-body method, we contrast this model with the traditional six-axis rigid industrial robot arm, revealing that two-segment extensible continuum manipulators exhibit limited rotation angles around the directional vector of their tips, thereby showcasing reduced dexterity. By pre-constraining five degrees-of-freedom (DOF) and addressing the definition of the remaining DOF, we streamline the IK-solving process, resulting in minimal computational overhead suitable for a wide range of applications. This model promises a robust and real-time approach for controlling two-segment extensible continuum manipulators, enhancing their operational efficiency and effectiveness.

Although the soft robot is gaining considerable popularity in dexterous and safe manipulation, accurate motion control is still an open problem to be explored. Recent investigations suggest that reinforcement learning (RL) is a promising solution but lacks efficient adaptability for Sim2Real transfer or environment variations. In this paper, we present a deep deterministic policy gradient (DDPG)-based control system for the continuous task-space manipulation of soft robots. Domain randomization is adopted in simulation for fast control-policy initialization, while an offline retraining strategy is utilized to update the controller parameters for incremental learning. The experiments demonstrate that the proposed RL controller can track a moving target accurately (with RMSE of 1.26 mm), and accommodate to external varying load effectively (with ~30% RMSE reduction after retraining). Comparisons among the proposed RL controller and other supervised-learning-based controllers in handling additional tip load were also conducted. The results support that our RL method is appropriate for automatic learning such that there is no need of manual interference for data processing, particularly in cases with external disturbances and actuation redundancy.

Robotics is a relatively new and evolving technology being applied to manufacturing automation and is fast replacing the special-purpose machines or hard automation as it is often called. Demands for higher productivity, better and uniform quality products, and better working environments are primary reasons for its development. An industrial robot is a multifunctional and computer-controlled mechanical manipulator exhibiting a complex and highly nonlinear behavior. Even though most current robots have anthropomorphic configurations, they have far inferior manipulating abilities compared to humans. A great deal of research effort is presently being directed toward improving their overall performance by using optimal mechanical structures and control strategies. The optimal design of robot manipulators can include kinematic performance characteristics such as workspace, accuracy, repeatability, and redundancy. The static load capacity as well as dynamic criteria such as generalized inertia ellipsoid, dynamic manipulability, and vibratory response have also been considered in the design stages. The optimal control problems typically involve trajectory planning, time-optimal control, energy-optimal control, and mixed-optimal control. The constraints in a robot manipulator design problem usually involve link stresses, actuator torques, elastic deformation of links, and collision avoidance. This paper presents a review of the literature on the issues of optimum design and control of robotic manipulators and also the various optimization techniques currently available for application to robotics.

This book is the second edition of a textbook published in 1996 by McGraw Hill and originates from a graduate level course given by the authors at the University of Naples. The topics include kinematics, statics and dynamics of robot manipulators together with trajectory planning and active control. There are only minor additions the first edition, which are mainly the use of quaternion to describe the orientation of the end effector and a short description of a closed chain architecture for a manipulator (parallelogram arm). The book is largely devoted to serial manipulators, with special developments about active control including adaptative control, robust controls and stability analysis. Another strength of this book is the great number of problems proposed at the end of each chapter, together with a list of references related to it. The fundamental features covered by the text are illustrated on simple examples of serial manipulators (two-link planar arm, parallelogram arm) including analytical results and numerical tests.The book has nine chapters followed by three appendices. The first appendix is devoted to linear algebra, the second recalls some fundamental aspects of rigid body mechanics and the third gives some basic principles of feedback control of linear systems.Chapter one is an introduction to the study of robot manipulators, giving an interesting classification of their architectures, the corresponding workspace and describes the tasks for which theyare used. After some standard examples of industrial manipulators, bibliographical reference texts are proposed, including textbooks on modelling and control of robots, general books on robotics, specialized texts, scientific robotic reviews and some international conferences on robotics.Chapters two, three and four are devoted to mechanical modelling of robot manipulators. The fundamental basics of kinematics are given in chapter two, including the representation of finite rotations by Euler angles or unit quaternions, homogeneous transformations, Denavit-Hartenberg parameters and workspace. The direct and inverse kinematical problems are solved in analytical form for some typical manipulator structures. The differential kinematics of robots are presented in chapter three, with an introduction to the geometric and analytic Jacobian matrices, kinematic singularities and redundancy. The inverse kinematic problem is presented, with special attention to the case of redundant robots where the solution is obtained by a linear optimization problem leading to the introduction of the pseudo-inverse Jacobian matrix and to the solution of several objectives such as avoidance of collision with an obstacle or moving away from singularities. Several inverse kinematics algorithms are given wih an interesting application to a three-link planar arm. Finally, a property of kineto-statics duality is deduced from the principle of virtual work applied to an equilibrium configuration of the robot. Chapter four is a standard presentation of the derivation of the dynamical model by Lagrange formulation and then by the Newton-Euler method. In the Lagrange formulation method, the linearity with respect to inertial parameters is shown and a detailed formulation of the dynamical model is obtained for a two-link Cartesian arm, a two-link planar arm and a parallelogram arm. The problem of dynamic parameter identification is also briefly presented from a numerical point of view. The recursive algorithm constructed from the Newton-Euler formulation is presented and illustrated by considering a two-link planar arm. Finally, the operational space dynamic model is introduced.In chapter five, paths and trajectory planning in joints and in operational spaces are presented; several classical methods of interpolation are described.Chapter six is an extensive study of active control of manipulators. Several methods are presented, involving classical independent joint control, non-linear centralized control, robust control and adaptative control. Both joint-space control and operational-space control are studied together with stability analysis by using Liapounoff functions. An interesting application to the two-link planar arm already used shows the comparison between various control schemes.Chapter seven deals with interaction control of serial manipulators with the working environment. Several strategies involving compliance control, impedance control, force control and hybrid control are presented.Chapter eight describes the actuators and the sensors used in robotics. Several types of servomotors (electric and hydraulic) are presented, together with the model giving their input/output relationship. Several kinds of sensors are also described including encoders, tachometers, force and vision sensors.The last chapter gives a short presentation of the functional architecture of a robot's control system, including characteristics of the programing environment and the hardware architecture.In conclusion, the book provides a good insight about simulation and control of robot manipulators, with a detailed study of the various control strategies and several interesting and pedagogical applications. This book is an excellent review of the standard knowledge needed not only for graduate students but also for researchers interested in robot manipulators.M Pascal

This paper presents a method to perform closed-loop control on a trunk-like soft manipulator within its workspace based on the locally linearized dynamic Finite Element(FE) models and Gain Scheduling. The main idea of this method is to first divide the workspace of the controlled robot into several sub-workspaces where the corresponding FE models can be locally linearized in every single sub-workspace so that it becomes easy to control the trunk by gain-scheduling while crossing from one sub-workspace to another. In the following part, the proposed control method was derived first and then successfully implemented on the soft trunk robot in SOFA, an open-source and real-time FEM software. The stability of the control method was also proved as well. In the end, the experimental testing was conducted on a handmade soft trunk robot, and its results show promising feasibility of the control method.

Real-time motion planning and control of multi-joint robot manipulators are studied using neural networks. The proposed neural network approach consists of two modules: one for real-time collision-free motion planning, the other for real-time fine control of the robot manipulators. The motion planning module is a biologically inspired, parallel connected, topologically organised neural network, where each neuron is characterised by a shunting equation, whose state space is the robot configuration joint space. This module is capable of planning a real-time optimal path for robot manipulators through the dynamic activity landscape of the neural network. The motion control module involves of a feedforward neural network together with a traditional PD feedback loop. It is capable of achieving real-time fine motion control of robot manipulators under significant uncertainties and without any prior knowledge of the robot dynamics. This module can quickly compensate sudden changes in the robot dynamics. The real-time fine control of robot manipulators is achieved through the online learning of the neural network. Both the motion planning and motion control modules are computationally efficient, and their global stabilities are proved using Lyapunov stability theory.

This paper presents the inverse kinematic analysis of a planar bio-inspired soft robot in a discrete task space using artificial intelligence (AI). The soft robot manipulator comprises a stack of hydraulically amplified dielectric elastomer actuators, which exhibit snap-through instability, resulting in a discrete robot task space. The model simulates the manipulator with a series of adjustable-length rods connected with revolute joints and satisfies the piecewise constant curvature (PCC) requirement for each robot segment. The discrete task space of the robot was estimated using the Monte Carlo method. The inverse kinematic analysis was carried out through genetic algorithm optimization with an embedded forward kinematic model to minimize the error for both the position and the orientation of the robot's end-effector. We analyze the fully controlled robot at each discrete segment, and we compare the results with the reduced complexity model obtained by grouping the actuator in sets of different sizes. The algorithm showed excellent results and can potentially be used in the analysis of similar soft manipulators with discrete task spaces.