Compliant Motion Research Articles

Torque-Sensorless Control of Stepper Motors for Low-Cost Compliant Motion Generation

Actuators that can generate controllable compliant output motion are suitable for robots that need to interact safely with humans or the environment. To obtain the accurate output torque required to control the compliant motion, existing robotic actuators use one or more sensors to measure the deformation of a stiff or compliant element between the actuator and the output. The complexity, reliability, and cost of sensors are the current challenges. Deformation-based sensing also suffers from limited bandwidth and stability range. This paper proposes a torque-sensorless control method of stepper motors to generate compliant motion. Stepper motors are used because of their low cost and reliability. They also have a much higher torque-to-weight ratio and torque-to-rotor-inertia ratio than other DC motors. Torque and impedance controllers based on stepper motor current feedback are developed. Torque-sensorless impedance, and gripping force control experiments are provided to show the advantages of the proposed method. It is expected that the new control method for stepper motors can offer a competitive alternative when cost, stability, or bandwidth of complaint actuators are the major concerns.

The Operational Space Formulation on Humanoids Considering Joint Stiffness and Bandwidth Limit

As the applications of robots increase, mobility and capability of interaction in unstructured environments have become important. Hence, humanoid robots have been developed to have these capabilities, and recently, compliant motions for safe contact have been studied. The Operational Space Formulation can be an effective method for humanoid robots to realize compliant motion via unified motion and force control. Nevertheless, the implementation of the Operational Space Formulation on humanoid robots has not been successful. One of the challenging issues is low position tracking performance. In this study, a control method for solving this problem is proposed by considering the joint stiffness and joint bandwidth limits. To consider joint stiffness, the Operational Space Formulation was derived using flexible joint robot dynamics. Additionally, an extra torque generation method, which penalizes low performance actuators, is utilized to account for the bandwidth limits. Neither methods requires additional sensors or joint space controllers. Hence, they can be useful for conventional humanoid robots composed of electric motors and gears. By integrating the two methods, the position tracking performance of the Operational Space Formulation on the humanoid robot was significantly improved. The proposed method was demonstrated via position and orientation control experiments using our humanoid robot, TOCABI.

Effect of combination of flexure joints and material used on performance of compliant pantograph mechanism

Compliant mechanism transfers motion or force using the elastic deflection of flexures or the whole mechanism (distributed). Pantograph is a widely proven mechanism which can imitate motion. The compliant pantograph models proposed by earlier researcher are limited to use of all circular flexure hinges; with some reporting geometric amplification (GA) around 2. Thus the present research work is an attempt towards using different combinations of flexures viz. circular, elliptical and parabolic deployed (for proposed six models), while developing the compliant pantograph (CP) in order to improve the GA. Also the effect of material Stainless Steel (SS) and Mild Steel (MS) on the performance of CP is presented. The finite element analysis is undertaken and output displacement is estimated for pre-decided range of input motion The maximum GA achieved for one of the combinations (combination no. 4) using SS is 2.57 wherein the flexures considered consist of two Elliptical and four circular flexures. Depending on desirable output range any of the above models can be used in micro stages/slides and many more precision applications.

Phase-Synchronized Learning of Periodic Compliant Movement Primitives (P-CMPs)

Autonomous trajectory and torque profile synthesis through modulation and generalization require a database of motion with accompanying dynamics, which is typically difficult and time-consuming to obtain. Inspired by adaptive control strategies, this paper presents a novel method for learning and synthesizing Periodic Compliant Movement Primitives (P-CMPs). P-CMPs combine periodic trajectories encoded as Periodic Dynamic Movement Primitives (P-DMPs) with accompanying task-specific Periodic Torque Primitives (P-TPs). The state-of-the-art approach requires to learn TPs for each variation of the task, e.g., modulation of frequency. Comparatively, in this paper, we propose a novel P-TPs framework, which is both frequency and phase-dependent. Thereby, the executed P-CMPs can be easily modulated, and consequently, the learning rate can be improved. Moreover, both the kinematic and the dynamic profiles are parameterized, thus enabling the representation of skills using corresponding parameters. The proposed framework was evaluated on two robot systems, i.e., Kuka LWR-4 and Franka Emika Panda. The evaluation of the proposed approach on a Kuka LWR-4 robot performing a swinging motion and on Franka Emika Panda performing an exercise for elbow rehabilitation shows fast P-CTPs acquisition and accurate and compliant motion in real-world scenarios.

Learning compliant robotic movements based on biomimetic motor adaptation

It is one of the great challenges for a robot to learn compliant movements in interaction tasks. The robot can easily acquire motion skills from a human tutor by kinematics demonstration, however, this becomes much more difficult when it comes to the compliant skills. This paper aims to provide a possible solution to address this problem by proposing a two-stage approach. In the first stage, the human tutor demonstrates the robot how to perform a task, during which only motion trajectories are recorded without the involvement of force sensing. A dynamical movement primitives (DMPs) model which can generate human-like motion is then used to encode the kinematics data. In the second stage, a biomimetic controller, which is inspired by the neuroscience findings in human motor learning, is employed to obtain the desired robotic compliant behaviors by online adapting the impedance profiles and the feedforward torques simultaneously. Several tests are conducted to validate the effectiveness of the proposed approach.

Numerical Framework and Design Optimization of an Intrinsically Compliant 3-DOF Parallel Robot

Abstract Parallel robots are multiple degrees of freedom (DOFs) systems that are typically used in applications characterized by enhanced accuracy, rigidity, and large force requirements within a compact workspace. In the present research, an intrinsically compliant parallel robot with 3-DOFs, actuated using four pneumatic muscle actuators (PMA), is conceptualized, developed, and analyzed. Despite many benefits, parallel robots also offer certain challenges that arise from the highly coupled and nonlinear motion of their actuators. The small workspace of parallel robots has many singularities and solving a closed-form forward kinematics (FK) for its end-effector motion is complicated. The PMAs can provide intrinsically compliant robotic motions, however, since they are flexible, their unilateral actuation also poses constraints on the achievable DOFs. The present research focuses on analyzing kinematics and dynamics of the developed parallel robot incorporating the stiffness together with force closure analyses besides suggesting design improvements as a consequence of the singularity analysis. Design synthesis and multi-criteria optimization have been performed to obtain a robot design which may provide higher accuracies (near unity condition number), quick response to external wrench (stiffness and rigidity), and reduced actuator force requirements. SPEA2 (Improved Strength Pareto Evolutionary Algorithm) has been implemented to carry out the simultaneous optimization of design objectives and provide Pareto optimal design solutions.

Adaptive motion intent understanding–based control of human–exoskeleton system

Although human–exoskeleton systems have tremendous potentials for rehabilitation training, transparent human–exoskeleton interaction has not been achieved for late-phase rehabilitation. To cope with this challenge, an adaptive pilot intent prediction–based control algorithm is proposed to achieve compliant motion coordination. A novel surface electromyography signal processing method providing definite physical meaning and high computational efficiency is derived. The state space model of human musculoskeletal system is developed with the input of the processed surface electromyography. Adaptive parameter estimation is employed to cope with the emerging dynamics. It integrates the advantages of surface electromyography signals which imply muscle contraction in advance and force signals with high stability. Based on the coupled human–exoskeleton system dynamics, transparent control of a human–exoskeleton system is realized. Experimental results show that the computational efficiency of the proposed surface electromyography processing method is 129% higher than the energy kernel method, and the maximum interactive force is reduced about 15 N compared with impedance control.

Lio-A Personal Robot Assistant for Human-Robot Interaction and Care Applications.

Lio is a mobile robot platform with a multi-functional arm explicitly designed for human-robot interaction and personal care assistant tasks. The robot has already been deployed in several health care facilities, where it is functioning autonomously, assisting staff and patients on an everyday basis. Lio is intrinsically safe by having full coverage in soft artificial-leather material as well as collision detection, limited speed and forces. Furthermore, the robot has a compliant motion controller. A combination of visual, audio, laser, ultrasound and mechanical sensors are used for safe navigation and environment understanding. The ROS-enabled setup allows researchers to access raw sensor data as well as have direct control of the robot. The friendly appearance of Lio has resulted in the robot being well accepted by health care staff and patients. Fully autonomous operation is made possible by a flexible decision engine, autonomous navigation and automatic recharging. Combined with time-scheduled task triggers, this allows Lio to operate throughout the day, with a battery life of up to 8 hours and recharging during idle times. A combination of powerful computing units provides enough processing power to deploy artificial intelligence and deep learning-based solutions on-board the robot without the need to send any sensitive data to cloud services, guaranteeing compliance with privacy requirements. During the COVID-19 pandemic, Lio was rapidly adjusted to perform additional functionality like disinfection and remote elevated body temperature detection. It complies with ISO13482 - Safety requirements for personal care robots, meaning it can be directly tested and deployed in care facilities.

Mechanical Impedance Control of Cooperative Robot During Object Manipulation Based on External Force Estimation Using Recurrent Neural Network

This paper proposes a compliant motion control for human-cooperative robots to absorb collision force when persons accidentally touch the robots even while the robot is manipulating an object. In the proposed method, an external force estimator, which can distinguish the net external force from the object manipulation force, is realized using an inverse dynamics model acquired by a recurrent neural network (RNN). By implementing a mechanical impedance control to the estimated external force, the robot can quickly and precisely carry the object keeping the mechanical impedance control functioned and can generate a compliant motion to the net external force only when the person touches it during manipulation. Since the proposed method estimates the external force from the generalized force based on the learned inverse dynamics, it is not necessary to install any sensors on the manipulated object to measure the external force. This allows the robot to detect the collision even when the person touches anywhere on the manipulated object. The RNN inverse dynamics model is evaluated by the leave-one-out cross-validation and it was found that it works well for unknown trajectories excluded from the learning process. Although the details were omitted due to the limitation of the page length, similar to the simulations, the RNN inverse dynamics model was evaluated using unknown trajectories in the six degree-of-freedom experiments, and it has been verified that it functions properly even for the unknown trajectories. Finally, the validity of the proposed method has been confirmed by experiments in which a person touches a robot while it is manipulating an object with six degrees of freedom.

Distributed computation and control of robot motion dynamics on FPGAs

Driven by advances in miniaturized electronics, many robotic systems today consist of modular hardware components. This leads to numerous computing units that are distributed within such systems. In order to make better use of such hardware structures from a computational point of view, the implementation of classical control approaches should be reconsidered. Following this idea, a method for distributed computation of motion dynamics of robotic systems using Field Programmable Gate Arrays is discussed. In this approach, local low-level actuator controllers are regarded as interconnected nodes, which are aware of their actuated degree of freedom and the resulting motion of the attached rigid body link element. A modified recursive Newton–Euler algorithm is computed by these nodes, where each node only exchanges data with the neighboring nodes. In the computations, a linear dependency on the dynamic parameters is kept in order to simplify the direct use of dynamic parameters estimated from experimental data. Implementation details and experimental results using a robotic manipulator arm are presented. The experiments show that the method allows compliant motion control. Payloads and external forces acting on a link are considered in the distributed computation of the model by informing the node controlling the respective link of the additional forces.

Multi-hierarchy interaction control of a redundant robot using impedance learning

The control of robots with a compliant joint motion is important for reducing collision forces and improving safety during human robot interactions. In this paper, a multi-hierarchy control framework is proposed for the redundant robot to enable the robot end-effector to physically interact with the unknown environment, while providing compliance to the joint space motion. To this end, an impedance learning method is designed to iteratively update the stiffness and damping parameters of the end-effector with desired performance. In addition, based on a null space projection technique, an extra low stiffness impedance controller is included to improve compliant joint motion behaviour when interaction forces are acted on the robot body. With an adaptive disturbance observer, the proposed controller can achieve satisfactory performance of the end-effector control even with the external disturbances in the joint space. Experimental studies on a 7 DOF Sawyer robot show that the learning framework can not only update the target impedance model according to a given cost function, but also enhance the task performance when interaction forces are applied on the robot body.

Physic-based and control-oriented modeling based robust control for soft dielectric elastomer actuator

Dielectric elastomer actuators (DEA) have attracted increasing attention in the field of soft robotics for use in generating soft and compliant motions. However, owing to their strong viscoelasticity, nonlinearity, and uncertainty, it is often difficult to gain high-precision tracking control. Here, a physic-based and control-oriented modeling based robust control is proposed to alleviate this difficulty in tracking. First, a conical DEA is designed, along with a physics-based and control-oriented model used to describe its dynamic behaviors. After this, a two-step identification method is proposed to obtain the model’s parameters. Using the model, a robust control approach is then developed to achieve satisfactory control performance—even when tracking multi-frequency and wide-range reference trajectory under uncertain conditions. Its stability is then analyzed and proofed. Finally, we conduct experiments to demonstrate and test the effectiveness of the actuator.

A sEMG-Based Shared Control System With No-Target Obstacle Avoidance for Omnidirectional Mobile Robots

We propose a novel shared control strategy for mobile robots in a human-robot interaction manner based on surface eletromyography (sEMG) signals. For security reasons, an obstacle avoidance scheme is introduced to the shared control system as collision avoidance guidance. The motion of the mobile robot is a resultant of compliant motion control and obstacle avoidance. In the mode of compliant motion, the sEMG signals obtained from the operator's forearms are transformed into human commands to control the moving direction and linear velocity of the mobile robot, respectively. When the mobile robot is blocked by obstacles, the motion mode is converted into obstacle avoidance. Aimed at the obstacle avoidance problem without a specific target, we develop a no-target Bug (NT-Bug) algorithm to guide the mobile robot to avoid obstacles and return to the command line. Besides, the command moving direction given by the operator is taken into consideration in the obstacle avoidance process to plan a smoother and safer path for the mobile robot. A model predictive controller is exploited to minimize the tracking errors. Experiments have been implemented to demonstrate the effectiveness of the proposed shared control strategy and the NT-Bug algorithm.

A Gesture-Based Teleoperation System for Compliant Robot Motion

Currently, the gesture-based teleoperation system cannot generate precise and compliant robot motions because human motions have the characteristics of uncertainty and low-resolution. In this paper, a novel, gesture-based teleoperation system for compliant robot motion is proposed. By using the left hand as the commander and the right hand as a positioner, different operation modes and scaling ratios can be tuned on-the-fly to meet the accuracy and efficiency requirements. Moreover, a vibration-based force feedback system was developed to provide the operator with a telepresence capability. The pick-and-place and peg-in-hole tasks were used to test the effectiveness of the teleoperation system we developed. The experiment results prove that the gesture-based teleoperation system is effective at handling compliant robot motions.

On Robot Compliance: A Cerebellar Control Approach.

The work presented here is a novel biological approach for the compliant control of a robotic arm in real time (RT). We integrate a spiking cerebellar network at the core of a feedback control loop performing torque-driven control. The spiking cerebellar controller provides torque commands allowing for accurate and coordinated arm movements. To compute these output motor commands, the spiking cerebellar controller receives the robot's sensorial signals, the robot's goal behavior, and an instructive signal. These input signals are translated into a set of evolving spiking patterns representing univocally a specific system state at every point of time. Spike-timing-dependent plasticity (STDP) is then supported, allowing for building adaptive control. The spiking cerebellar controller continuously adapts the torque commands provided to the robot from experience as STDP is deployed. Adaptive torque commands, in turn, help the spiking cerebellar controller to cope with built-in elastic elements within the robot's actuators mimicking human muscles (inherently elastic). We propose a natural integration of a bioinspired control scheme, based on the cerebellum, with a compliant robot. We prove that our compliant approach outperforms the accuracy of the default factory-installed position control in a set of tasks used for addressing cerebellar motor behavior: controlling six degrees of freedom (DoF) in smooth movements, fast ballistic movements, and unstructured scenario compliant movements.

Flexible Robot With Variable Stiffness in Transoral Surgery

To possess sufficient compliance while keeping an acceptable stiffness level for manipulation with precision, transoral robotic surgery (TORS) demands a flexible robotic system with variable stiffness (VS) as presented in this paper. In our study, a flexible three-prismatic-universal parallel mechanism employs superelastic nickel–titanium rods to achieve compliant movements beyond the conventional rigid-body parallel mechanisms. With a compact structure and flexible-shaft transmission, the adjustable tube-based VS mechanism allows the stiffness of the manipulator to be continuously tuned in real time according to the surgical requirements. A stiffness model is derived to evaluate the stiffness of the manipulator quantitatively. A parallel mechanism with three prismatic-revolute-spherical chains is adopted as the master device, to improve the maneuverability and decrease the learning curve for less experienced surgeons. The TORS manipulators are characterized and verified in the laboratory and cadaveric trials, showing the VS and the execution of the master–slave teleoperated configuration. Furthermore, the cadaveric trials attested the effectiveness of the VS mechanism and the preclinical feasibility of the robotic system.

Bayesian Tactile Exploration for Compliant Docking With Uncertain Shapes

This paper presents a Bayesian approach for active tactile exploration of a planar shape in the presence of both localization and shape uncertainty. The goal is to dock the robot's end-effector against the shape-reaching a point of contact that resists a desired load-with as few probing actions as possible. The proposed method repeatedly performs inference, planning, and execution steps. Given a prior probability distribution over object shape and sensor readings from previously executed motions, the posterior distribution is inferred using a novel and efficient Hamiltonian Monte Carlo method. The optimal docking site is chosen to maximize docking probability, using a closed-form probabilistic simulation that accepts rigid and compliant motion models under Coulomb friction. Numerical experiments demonstrate that this method requires fewer exploration actions to dock than heuristics and information-gain strategies.

Study on motion performance of robot-aided passive rehabilitation exercises using novel dynamic motion planning strategy

The motion rehabilitation training robot is developed to help patients with motion dysfunction recover their motor function by providing a large amount of repetitive robot-aided exercise. To achieve stable and smooth robot-aided exercises for stroke patients, a motion control method with a novel dynamic motion planning strategy is proposed. The physical state of the training limb is assessed real time during the rehabilitation exercises. The dynamic motion planning strategy is developed by employing a suitable interpolation method dynamically corresponding to the physical state of the training limb to plan a trajectory tracking system that completely utilizes different interpolation characteristics to manage the movement in accordance with the time-varying physical state of the training limb. Concurrently, a position-based impedance control is adopted to achieve compliant movement. Functional (quantitative and qualitative) and clinical experiments are conducted on a four-degree-of-freedom whole-arm manipulator upper limb rehabilitation robot to verify the effectiveness of the control method designed with the dynamic motion planning strategy. The results indicate that the proposed control strategy can exhibit better performances in terms of the stability and smoothness.

A Novel Wire-Driven Variable-Stiffness Joint Based on a Permanent Magnetic Mechanism

The variable-stiffness joint (VSJ) plays an important role in creating compliant and powerful motions. This paper presents a novel wire-driven VSJ based on a permanent magnetic mechanism (PMM). The proposed joint regulates the joint stiffness with lower energy consumption through a wide range via the permanent magnetic mechanism. This effect possibly depends on the novel nonlinear combination of a permanent magnet-spring and wire-driven system that achieves the same stiffness with lower wire tension. A trapezoidal layout of the joint is proposed. Because of the relationship among the stiffness, the position of the joint and the stiffness of the PMM, the stiffness model is also been established. Based on this model, the decoupling controller is built to independently control the position and stiffness of the joint. Experiments show that the VSJPMM achieves position and stiffness independently and also reduces energy and power required to regulate the stiffness compared with the traditional approach. In addition, the proposed mechanism displays a powerful motion and short stiffness adjustment time.

Improved Human–Robot Collaborative Control of Redundant Robot for Teleoperated Minimally Invasive Surgery

An improved human–robot collaborative control scheme is proposed in a teleoperated minimally invasive surgery scenario, based on a hierarchical operational space formulation of a seven-degree-of-freedom redundant robot. Redundancy is exploited to guarantee a remote center of motion (RCM) constraint and to provide a compliant behavior for the medical staff. Based on the implemented hierarchical control framework, an RCM constraint and a safe constraint are applied to the null-space motion to achieve the surgical tasks with human–robot interaction. Due to the physical interactions, safety and accuracy of the surgery may be affected. The control framework integrates an adaptive compensator to enhance the accuracy of the surgical tip and to maintain the RCM constraint in a decoupled way avoiding any physical interactions. The system performance is verified on a patient phantom. Compared with the methods proposed in the literature, results show that the accuracy of both the RCM constraint and the surgical tip is improved. The compliant swivel motion of the robot arm is also constrained in a defined area, and the interaction force on the abdominal wall becomes smaller.