Compliant Manipulator Research Articles

A compliant and strong robotic manipulator that looks and works like an elephant trunk

A compliant and strong robotic manipulator that looks and works like an elephant trunk

Constraint-Aware Policy for Compliant Manipulation

Robot manipulation in a physically constrained environment requires compliant manipulation. Compliant manipulation is a manipulation skill to adjust hand motion based on the force imposed by the environment. Recently, reinforcement learning (RL) has been applied to solve household operations involving compliant manipulation. However, previous RL methods have primarily focused on designing a policy for a specific operation that limits their applicability and requires separate training for every new operation. We propose a constraint-aware policy that is applicable to various unseen manipulations by grouping several manipulations together based on the type of physical constraint involved. The type of physical constraint determines the characteristic of the imposed force direction; thus, a generalized policy is trained in the environment and reward designed on the basis of this characteristic. This paper focuses on two types of physical constraints: prismatic and revolute joints. Experiments demonstrated that the same policy could successfully execute various compliant manipulation operations, both in the simulation and reality. We believe this study is the first step toward realizing a generalized household robot.

A Large-Scale Inflatable Robotic Arm Toward Inspecting Sensitive Environments: Design and Performance Evaluation

As a subdomain of soft robots, inflatable robotic arms (IRAs) offer a promising alternative to inspect the sensitive environments that entail highly compliant manipulation, e.g., disaster rescue or archaeology exploration. This article presents a lightweight (only 843 g in weight) and 10.1-m-long deployable IRA that is a portable integration of mechanisms, electronics and pneumatics. The actuation, fabrication and sensing of the IRA are firstly elaborated on from the perspective of joint modularization. Antagonist pneumatic artificial muscles (PAMs) are leveraged to enable the 10-degrees-of-freedom (-DOF) motion of the thin-walled and Helium-inflated body. The hardware-in-the-loop system is established, with the pneumatic components integrated into a compact box. Given the limited position accuracy of the environmentally compliant IRA, a custom-designed joystick input interface is incorporated to achieve human-in-the-loop teleoperation, thereby enhancing the situational awareness. The control architecture, covering the actuator, joint and task spaces, is employed to address the model uncertainties that are induced by both the IRA itself and the cluttered environments. Long-range inspection demonstrations that associate with obstacle avoidance and high-precision navigation are performed in typical simulation scenes like complex terrains and relic/plant environments. Experimental results ultimately reveal the promising performance of the developed IRA, enabling its future deployment in inspection.

Model-based variable impedance learning control for robotic manipulation

The capability to adapt compliance by varying muscle stiffness is crucial for dexterous manipulation skills in humans. Incorporating compliance in robot motor control is crucial for enabling real-world force interaction tasks with human-like dexterity. In this study, we introduce a novel approach, we call “deep Model Predictive Variable Impedance Controller (MPVIC)” for compliant robotic manipulation, which combines Variable Impedance Control with Model Predictive Control (MPC). The method involves learning a generalized Cartesian impedance model of a robot manipulator through an exploration strategy to maximize information gain. Within the MPC framework, this learned model is utilized to adapt the impedance parameters of a low-level variable impedance controller, thereby achieving the desired compliance behavior for various manipulation tasks without requiring retraining or finetuning. We assess the efficacy of the proposed deep MPVIC approach using a Franka Emika Panda robotic manipulator in simulations and real-world experiments involving diverse manipulation tasks. Comparative evaluations against model-free and model-based reinforcement learning approaches in variable impedance control are conducted, considering aspects such as transferability between tasks and performance. The results demonstrate the effectiveness and potential of the presented approach for advancing robotic manipulation capabilities.

Sliding-surface dynamic control of a continuum manipulator with large workspace

This paper presents the trajectory tracking control of a multi-section continuum and compliant manipulator with large workspace. Fluidic muscles are utilized as actuators in this manipulator and inertial sensors used to measure its shape in configuration space. The robot's large workspace signifies the effects of the inertial, gravitational, and applied external forces on the trajectory tracking control of the robot end-effector. In this study, a model-based sliding-surface controller is proposed by deriving a simplified Pseudo-Rigid Body (PRB) dynamic model, which utilizes modified constant curvature assumptions to describe the couplings between the robot sections and actuators. Simplification steps are proposed for a PRB dynamic model by eliminating negligible dynamic effects to achieve a computationally efficient implementation with real-time control performance. The unmodeled dynamics and the error of model simplifications are considered as system uncertainties and an integral sliding surface with a variable sliding gain is proposed to overcome these uncertain dynamics. The sliding gain considers changes in the upper bounds of uncertainties. The closed-loop system stability in trajectory tracking is mathematically proved based on the Lyapunov stability theorem. Experiments throughout the manipulator's large workspace in challenging configurations including sudden release of unknown loads at the robot tip also highlight the potential of our contributed controller.

Multifingered Robot Hand Compliant Manipulation Based on Vision-Based Demonstration and Adaptive Force Control.

Multifingered hand dexterous manipulation is quite challenging in the domain of robotics. One remaining issue is how to achieve compliant behaviors. In this work, we propose a human-in-the-loop learning-control approach for acquiring compliant grasping and manipulation skills of a multifinger robot hand. This approach takes the depth image of the human hand as input and generates the desired force commands for the robot. The markerless vision-based teleoperation system is used for the task demonstration, and an end-to-end neural network model (i.e., TeachNet) is trained to map the pose of the human hand to the joint angles of the robot hand in real-time. To endow the robot hand with compliant human-like behaviors, an adaptive force control strategy is designed to predict the desired force control commands based on the pose difference between the robot hand and the human hand during the demonstration. The force controller is derived from a computational model of the biomimetic control strategy in human motor learning, which allows adapting the control variables (impedance and feedforward force) online during the execution of the reference joint angles. The simultaneous adaptation of the impedance and feedforward profiles enables the robot to interact with the environment compliantly. Our approach has been verified in both simulation and real-world task scenarios based on a multifingered robot hand, that is, the Shadow Hand, and has shown more reliable performances than the current widely used position control mode for obtaining compliant grasping and manipulation behaviors.

Minimizing Energy Consumption of Elastic Robots in Repetitive Tasks

Energy consumption is an important issue in robotics. This article deals with the problem of reducing the energy consumption of compliant electro-mechanical systems while performing periodic tasks. After deriving performance indices to quantify the energy consumption of a mechanical system, we propose a method to determine both the optimal compliant actuation parameters, and link trajectories to minimize energy consumption. We show how this problem can be cast in a simpler one where the optimization regards only parameters that define the shape of periodic trajectories to be subsequently determined by using numerical optimization tools. Indeed, in our framework, the optimal stiffness and spring preload can be analytically obtained as a function of the desired link trajectories. We then provide simulations and experimental validations of the obtained results on a two-link compliant manipulator platform which performs a repetitive pick-and-place task. Our experiments show that the use of compliant actuators instead of rigid ones and the optimization of their compliant parameters give rise to an energy saving up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$62\%$</tex-math> </inline-formula> with respect to rigid actuation. Moreover, the simultaneous optimization of the compliant parameters and link trajectories provide an additional energy saving up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$20\%$</tex-math> </inline-formula> .

A Single-Fidelity Surrogate Modeling Method Based on Nonlinearity Integrated Multi-Fidelity Surrogate

Abstract Surrogate model provides a promising way to reasonably approximate complex underlying relationships between system parameters. However, the expensive modeling cost, especially in large problem sizes, hinders its applications in practical problems. To overcome this issue, with the advantages of the multi-fidelity surrogate (MFS) model, this paper proposes a single-fidelity surrogate model with a hierarchical structure, named nonlinearity integrated correlation mapping surrogate (NI-CMS) model. The NI-CMS model first establishes the low-fidelity model to capture the underlying landscape of the true function, and then, based on the idea of MFS model, the established low-fidelity model is corrected by minimizing the mean square error to ensure prediction accuracy. Especially, a novel MFS model (named NI-MFS), is constructed to enhance the stability of the proposed NI-CMS model. More specifically, a nonlinear scaling term, which assumes the linear combination of the projected low-fidelity predictions in a high-dimensional space can reach the high-fidelity level, is introduced to assist the traditional scaling term. The performances of the proposed model are evaluated through a series of numerical test functions. In addition, a surrogate-based digital twin of an XY compliant parallel manipulator is used to validate the practical performance of the proposed model. The results show that compared with the existing models, the NI-CMS model provides a higher performance under the condition of a small sample set, illustrating the promising potential of this surrogate modeling technique.

A Compliant Manipulator for Confined Space Tissue Diagnostics: Kinematic and Force Analyses and Initial Characterization Experiments

Abstract Minimally invasive procedures employ continuum manipulators, however internal human anatomy presents challenges relating to size, dexterity, and workspace for these manipulators. This manuscript presents modeling, kinematic analysis, prototyping, and characterization of a micro-robotic manipulator for transurethral palpation of bladder tissue. The proposed micro-robot consists of two subsystems: a tendon-driven continuum segment with an elastic tube encompassing each joint for compliance and structural integrity, and a hyper-spherical joint ensuring higher dexterity and manipulability with a comprehensive actuation and modeling approach. The forward kinematics follow the Denavit-Hartenberg formulation. A developed differential Jacobian inverse kinematics formulation prevents motion singularities for desired poses while operating in the confined space. The simulated kinematic results confirm the dexterity and reach of the proposed micro-robot. A strain energy quasi-static model is developed for a single continuum module. The model is evaluated for tension-bend angle relationships as function of tube material and geometry, and joint length. Limited functionality continuum modules (4mm outside diameter) with four different joint lengths, (3, 6, 9, 12) mm, are prototyped for tension-bend angle characterization using a computer vision outfitted experimental setup. An equivalent shear modulus relationship for the elastic tube for selected joint length values and bend angles is developed using experimental results. The tension-bend angle response is nonlinear and function of tube properties, joint geometry, and their interactions. The comparison of the experimental and quasi-static model results shows high fidelity for use in predicting the robot continuum segment behavior.

Iterative Learning Control for Compliant Underactuated Arms

Operations involving safe interactions in unstructured environments require robots with adapting behaviors. Compliant manipulators are a promising technology to achieve this goal. Despite that, some classical control problems such as following a trajectory are still open. A typical solution is to compensate the system dynamics with feedback loops. However, this solution increases the effective robot stiffness and jeopardizes the safety property provided by the compliant design. On the other hand, purely feedforward approaches can achieve good tracking performance while preserving the robot intrinsic compliance. However, a feedforward control framework for robots with passive elastic joints is still missing. This article presents an iterative learning control algorithm for purely feedforward trajectory tracking for compliant underactuated arms. Each arm is composed of active elastic joints and a generic number of passive ones connected through rigid links. We prove the convergence of the iterative method, also in the presence of uncertainties and bounded disturbances. Different output functions are analyzed providing conditions, based on the system inertial properties that ensure the algorithm applicability. Additionally, an automatic selection of the learning gain is proposed. Finally, we extensively validate the theoretical results with simulations and experiments.

High performance assembly of complex structural parts in special environments – research on space manipulator assisted module docking method

PurposeCompared with the robotic manipulation in structured environment, high performance assembly of complex parts in extreme special environment is facing great challenges because of the uncertainty in the environment, and the decline of the control accuracy of the robot and the sensor accuracy. The assembly and construction of the space station is a typical case. An important step in the construction of the space station is the module positioning and docking with the auxiliary of the space manipulator. The operation of the manipulator is faced with many problems, such as low sensing information accuracy, large end position deviation and the requirement of weak impact in the docking process. The purpose of this paper is to design a docking method at the strategy level to effectively solve the problems that may be faced in the docking process.Design/methodology/approachInspired by the research of robotic high-precision compliant assembly, this paper introduces the concept of Attractive Region in Environment (ARIE) into the space manipulator–assisted module docking. The contact configuration space of the docking mechanism and the existence of ARIE are systematically analyzed. The docking strategy based on ARIE framework is proposed, in which the impedance control is used to ensure the weak impact during the docking process.FindingsFor the androgynous peripheral spacecraft docking mechanism, a large range of attractive region exists in the high-dimensional contact configuration space. The docking strategy based on ARIE framework can be designed according to the geometric characteristics of the constraint region and the structural characteristics of the docking mechanism. The virtual models and the simulation environment are established, and the effectiveness of the proposed method is preliminarily verified.Originality/valueBased on the research results of robotic precision compliant manipulation, in this paper, the theory of ARIE is first systematically applied to the analysis of spacecraft docking problem and the design of docking scheme. The effectiveness of the proposed docking method is preliminarily verified for the requirements of large position tolerance and weak impact. The research results will provide theoretical support and technical reference for the assembly and construction of space station and other space manipulator operations.

Compliant manipulator design method (COMAD) for the type synthesis of all serial and parallel multi-DoF compliant mechanisms, with example of a Schönflies motion generator

This article presents the compliant manipulator design method (COMAD) for the synthesis of serial and parallel multi-DoF compliant mechanisms. Currently, the freedom and constraint topology (FACT)-method results in flexure systems being a serial kinematic solution for multi-DoF motions. In the COMAD method parallel solutions are included too through 3 steps: (1) obtaining the serial and parallel kinematic solutions for an intended set of end-effector DoFs with the type synthesis of legs-method; (2) transforming each legtype into a flexure leg by using the FACT method; (3) combining legs in parallel to obtain complete compliant mechanism designs. It was applied for a compliant Schönflies motion generator – having three translations and one rotation – resulting in 5 different 4-DoF flexure legs. 4 designs were new compared to the result obtained using the FACT method. Then, a set of legs was combined in parallel resulting in a compliant Schönflies mechanism, which was manufactured. Its mobility was experimentally evaluated by modal analysis. The intended motions separately became visible during its first four eigenmodes. They are the mechanism’s DoFs as their stiffness is relatively low.

Modeling of rigid-link and compliant joint manipulator using the discrete body dynamics method

Modeling of rigid-link and compliant joint manipulator using the discrete body dynamics method

Design and Development of the Transmission for a Fully Actuated 5-Degrees-of-Freedom Compliant Robot Manipulator With a Single Motor

AbstractThis article presents a novel architecture for the actuation and transmission system of a 5-degrees-of-freedom (DOFs) compliant robot manipulator. The compliant behavior of the robot is achieved using inherently compliant magneto-rheological (MR) clutches introduced in antagonistic pairs in every joint of the manipulator. All five antagonistic MR clutch pairs are driven using a single brushless DC motor located in the base of the robot. The MR clutch pairs are coupled to the motor through a system of shafts, belts, and gears. Several possible architectures for realizing a suitable drive train are presented, and the advantages and disadvantages of each concept are analyzed. The most efficient architecture for the drive train is selected to complete the design of the manipulator. The kinematics of the manipulator using the adopted architecture is further analyzed, and the workspace of the system is presented. To the best of our knowledge, this is the first 5-DOF, fully actuated, compliant robotic manipulator that uses a single DC motor to achieve five independent axes of rotations.

Design and testing of a large-workspace XY compliant manipulator based on triple-stage parallelogram flexure

This paper proposes a new XY compliant parallel manipulator with large workspace and low coupling motion by resorting to planar arrangement of multistage parallelogram mechanisms. The triple-stage parallelogram flexure is chosen as the basic mechanism for motion transmission and decoupling. Its fixed and moving ends adopt planar and spatial connections, respectively. By this connecting scheme, more types of general multistage parallelogram flexures can be constructed. The driving stiffness and resonant frequency of the manipulator are derived based on statics analysis of theoretical mechanics and dynamics analysis via Lagrangian equation. The accuracy of the analytical model is confirmed by performing finite element analysis simulation, and the performance variances of three manipulators are compared. Moreover, a prototype of the XY compliant manipulator has been fabricated and tested. Results show that the workspace of the parallel manipulator is 1.51 cm × 1.48 cm, and the coupling ratios of the two axes are 1.11% and 1.06%, respectively. The volume ratio is introduced as a compactness indicator for comparison study versus existing designs. The centimeter-level stroke can promote the application of XY parallel manipulators in more industrial scenarios.

Compliant Manipulation With Quasi-Rigid Docking for Underwater Structure Inspection

Compliant Manipulation With Quasi-Rigid Docking for Underwater Structure Inspection

Application of particle swarm optimization algorithm in manipulator compliance control

Application of particle swarm optimization algorithm in manipulator compliance control

Application of particle swarm optimisation algorithm in manipulator compliance control

Application of particle swarm optimisation algorithm in manipulator compliance control

Design, Sensing, and Control of a Magnetic Compliant Continuum Manipulator

Continuum manipulators coupled with magnetic actuation have great potential as steerable instruments for diverse surgical applications. They can be maneuvered inside the human body to reach difficult-to-access surgical sites with contactless actuation. This paper presents a new design of a compliant continuum manipulator of diameter 3 mm and length 70 mm, capable of spatial bending under magnetic actuation. A quasi-static model is developed to estimate the 3D motion of the manipulator. Experiments report an overall mean error in whole shape estimation of the manipulator between the model and the ground truth of 1.7 mm and 4.8 mm, when suspended vertically and horizontally from its base, respectively. Furthermore, fiber Bragg grating (FBG) sensors are integrated with the manipulator to enable shape sensing. Closed-loop control is demonstrated to trace different trajectories with the tip of the manipulator. A square trajectory and a straight line trajectory are generated with an average error in tip position of 4.1 mm between the desired and estimated positions. The potential of the manipulator as a steerable instrument is validated by maneuvering it inside phantoms of a bifurcating arterial system and a heart with visual guidance from a miniature camera.

Bayesian optimization with unknown constraints in graphical skill models for compliant manipulation tasks using an industrial robot.

This article focuses on learning manipulation skills from episodic reinforcement learning (RL) in unknown environments using industrial robot platforms. These platforms usually do not provide the required compliant control modalities to cope with unknown environments, e.g., force-sensitive contact tooling. This requires designing a suitable controller, while also providing the ability of adapting the controller parameters from collected evidence online. Thus, this work extends existing work on meta-learning for graphical skill-formalisms. First, we outline how a hybrid force–velocity controller can be applied to an industrial robot in order to design a graphical skill-formalism. This skill-formalism incorporates available task knowledge and allows for online episodic RL. In contrast to the existing work, we further propose to extend this skill-formalism by estimating the success probability of the task to be learned by means of factor graphs. This method allows assigning samples to individual factors, i.e., Gaussian processes (GPs) more efficiently and thus allows improving the learning performance, especially at early stages, where successful samples are usually only drawn in a sparse manner. Finally, we propose suitable constraint GP models and acquisition functions to obtain new samples in order to optimize the information gain, while also accounting for the success probability of the task. We outline a specific application example on the task of inserting the tip of a screwdriver into a screwhead with an industrial robot and evaluate our proposed extension against the state-of-the-art methods. The collected data outline that our method allows artificial agents to obtain feasible samples faster than existing approaches, while achieving a smaller regret value. This highlights the potential of our proposed work for future robotic applications.