Interaction Force Control Research Articles

Vision-based adaptive LT sliding mode admittance control for collaborative robots with actuator saturation

Abstract In this paper, we propose a novel vision-based adaptive leakage-type (LT) sliding mode admittance control for actuator-constrained collaborative robots to realize the synchronous control of the precise path following and compliant interaction force. Firstly, we develop a vision-admittance-based model to couple the visual feedback and force sensing in the image feature space so that a reference image feature trajectory can be obtained concerning the contact force command and predefined trajectory. Secondly, considering the system uncertainty, external disturbance, and torque constraints of collaborative robots in reality, we propose an adaptive sliding mode controller in the image feature space to perform precise trajectory tracking. This controller employs a leakage-type (LT) adaptive control law to reduce the side effects of system uncertainties without knowing the upper bound of system uncertainties. Moreover, an auxiliary dynamic is considered in this controller to overcome the joint torque constraints. Finally, we prove the convergence of the tracking error with the Lyapunov stability analysis and operate various semi-physical simulations compared to the conventional adaptive sliding mode and parallel vision/force controller to demonstrate the efficacy of the proposed controller. The simulation results show that compared with the controller mentioned above, the path following accuracy and interaction force control precision of the proposed controller increased by 50% and achieved faster convergence.

Research on mode adjustment control strategy of upper limb rehabilitation robot based on fuzzy recognition of interaction force

In the process of robot-assisted training for upper limb rehabilitation, a passive training strategy is usually used for stroke patients with flaccid paralysis. In order to stimulate the patient's active rehabilitation willingness, the rehabilitation therapist will use the robot-assisted training strategy for patients who gradually have the ability to generate active force. This study proposed a motor function assessment technology for human upper-limb based on fuzzy recognition on interaction force and human-robot interaction control strategy based on assistance-as-needed. A passive training mode based on the calculated torque controller and an assisted training mode combined with the potential energy field were designed, and then the interactive force information collected by the three-dimensional force sensor during the training process was imported into the fuzzy inference system, the degree of active participation σ was proposed, and the corresponding assisted strategy algorithms were designed to realize the adaptive adjustment of the two modes. The significant correlation between the degree of active participation σ and the surface electromyography signals (sEMG) was found through the experiments, and the method had a shorter response time compared to a control strategy that only adjusted the mode through the magnitude of interaction force, making the robot safer during the training process.

Control of the Probe-Sample Interaction Force at the Piconewton Scale by a Magnetic Microprobe in Aqueous Solutions

This article describes the design and implementation of a control system that controls the probe-sample interaction force at the piconewton scale by manipulating a magnetic microprobe in aqueous solutions under a microscope. The control system has two functions, namely accurate force generation by magnetic flux control and precise control of the probe-sample interaction force. Magnetic flux control uses an improved six-input-six-output digital control law along with a disturbance estimator to achieve two control objectives. First, the magnetic flux at each pole tip of a previously developed hexapole electromagnetic actuator can be individually and precisely controlled at frequencies over 4 kHz, and the experimental results precisely followed the theoretical predictions based on the design. Second, together with the optimized flux allocation, the flux control system generates precise magnetic forces, making the six-input hexapole actuator behave like a decoupled three-axis force generator with a bandwidth of more than 4 kHz. Interaction force control compares the measured deformation of the sample with the expected deformation, updated in real time by a deformation predictor, to generate the magnetic force to control the interaction between the probe and the sample. Digital control laws, the estimator, and the predictor are all implemented using a high-speed Field Programmable Gate Array (FPGA) system. Experiments confirm that through mathematical modeling, digital control technology, high-speed electronics, and real-time computation, accurate three-dimensional force generation at the piconewton scale with high bandwidth and precise interaction force control with zero-mean random error, attributed to random thermal force and measurement noise, are achieved.

Dynamic Motion Primitives-Based Trajectory Learning for Physical Human–Robot Interaction Force Control

One promising function of interactive robots is to provide a specific interaction force to human users. For example, rehabilitation robots are expected to promote patients' recovery by interacting with them with a prescribed force. However, motion uncertainties of different individuals, which are hard to predict due to the varying motion speed and noises during motion, degrade the performance of existing control methods. This paper proposes a method to learn a desired reference trajectory for a robot based on dynamic motion primitives (DMPs) and iterative learning (IL). By controlling the robot to follow the generated desired reference trajectory, the interaction force can achieve a desired value. In our proposed approach, DMPs are first employed to parameterize the demonstration trajectories of the human user. Then a recursive least square (RLS)-based estimator is developed and combined with the Adam optimization method to update the trajectory parameters so that the desired reference trajectory of the robot is iteratively obtained by resolving the DMPs. Since the proposed method parameterizes the trajectories depending on the phrase variable, it removes the essential assumption of traditional IL methods where the iteration period should be invariant, and thus has improved robustness compared with the existing methods. Experiments are performed using an interactive robot to validate the effectiveness of our proposed scheme.

AI-Powered Robust Interaction Force Control of a Cardiac Ultrasound Robotic System

AI-Powered Robust Interaction Force Control of a Cardiac Ultrasound Robotic System

Human–Robot Interaction Force Control of Series Elastic Actuator-Driven Upper Limb Exoskeleton Robot

Human–Robot Interaction Force Control of Series Elastic Actuator-Driven Upper Limb Exoskeleton Robot

Multi-directional Interaction Force Control with an Aerial Manipulator Under External Disturbances

To improve accuracy and robustness of interactive aerial robots, the knowledge of the forces acting on the platform is of uttermost importance. The robot should distinguish interaction forces from external disturbances in order to be compliant with the firsts and reject the seconds. This represents a challenge since disturbances might be of different nature (physical contact, aerodynamic, modeling errors) and be applied to different points of the robot. This work presents a new hbox {extended Kalman filter (EKF)} based estimator for both external disturbance and interaction forces. The estimator fuses information coming from the system’s dynamic model and it’s state with wrench measurements coming from a Force-Torque sensor. This allows for robust interaction control at the tool’s tip even in presence of external disturbance wrenches acting on the platform. We employ the filter estimates in a novel hybrid force/motion controller to perform force tracking not only along the tool direction, but from any platform’s orientation, without losing the stability of the pose controller. The proposed framework is extensively tested on an omnidirectional aerial manipulator (AM) performing push and slide operations and transitioning between different interaction surfaces, while subject to external disturbances. The experiments are done equipping the AM with two different tools: a rigid interaction stick and an actuated delta manipulator, showing the generality of the approach. Moreover, the estimation results are compared to a state-of-the-art momentum-based estimator, clearly showing the superiority of the EKF approach.

Visual-admittance-based model predictive control for nuclear collaborative robots

This paper presents a novel visual-admittance-based model predictive control scheme to cope with the problem of vision/force control and several constraints of a nuclear collaborative robotic visual servoing system. A visual-admittance model considering the desired image feature and force command in the image feature space is proposed. Moreover, a novel constraint scheme of the model predictive control (MPC) is proposed to cancel the overshoot in the interaction force control for most cases by taking the desired force command as the constraint of the proposed MPC. Via applying the robotic dynamic image-based visual servoing (IBVS) model, some other constraints, such as the actuator saturation, joint angle, and visual limits, can be satisfied simultaneously. The simulation results for the two-degrees-of-freedom (DOF) robot manipulator with an eye-to-hand camera are present to demonstrate the effectiveness of the proposed controller.

An adaptive impedance control for dual-arm manipulators incorporated with the virtual decomposition control

The interaction force control problem for the object manipulated by the dual-arm manipulator with the unknown environment is addressed in this paper. An adaptive dual-loop impedance control scheme is designed to meet the three control targets, including the trajectory tracking control of the object, the internal force control of the object with the two arms, and the interaction force control of the object with the unknown environment. Firstly, an adaptive variable impedance control method is proposed in the outer loop to copy with the unknown stiffness and position of the environment. Secondly, an impedance control scheme based on the internal force is designed in the inner loop to plan the internal force and the modified position supplied with the outer-loop control. Thirdly, to realize the reference trajectory supplied with the inner-loop control, the virtual decomposition control (VDC) method is incorporated, which is a recursive adaptive control progress, so that the unknown dynamics of the manipulators is acceptable and the computation complexity is also very low to satisfy the real-time control requirement. The stability verifications of the inner-loop impedance control and the VDC method are given based on the Lyapunov method, and the stability analysis of the outer-loop impedance control is obtained using the Routh criterion. Finally, a plane dual-arm robotic manipulator with three degrees of freedom is used to verify the effectiveness of the proposed control scheme.

A CoppeliaSim Dynamic Simulator for the Da Vinci Research Kit

The design of a physics-based dynamic simulator of a robot requires to properly integrate the robot kinematic and dynamic properties in a virtual environment. Naturally, the closer is the integrated information to the real robot properties, the more accurate the simulator predicts the real robot behaviour. A reliable robot simulator is a valuable asset for developing new research ideas; its use dramatically reduces the costs and it is available to all researchers. This letter presents a dynamic simulator of the da Vinci Research Kit (dVRK) patient-side manipulator (PSM). The kinematic and dynamic properties of the simulator rely on the parameters identified in Wang et al. With respect to the kinematic simulator previously developed by some of the authors, this work: (i) redefines the kinematic architecture and the actuation model by modeling the double parallelogram and the counterweight mechanism, to reflect the structure of the real robot; (ii) integrates the identified dynamic parameters in the simulation model. The obtained simulator enables the design and validation of control strategies relying on the robot dynamic model, including interaction force estimation and control, that are fundamental to guarantee safety in many surgical tasks.

Adaptive robust force control of an underactuated walking lower limb hydraulic exoskeleton

This paper presents an adaptive robust interaction force control algorithm for an underactuated walking lower limb hydraulic exoskeleton. In order to deal with the problem of passive joints (less of control inputs), holonomic constraints from the wearer are considered in system dynamics, which help transform the underactuated exoskeleton dynamics into fully actuated dynamics in Cartesian space. Based on the established dynamic model, adaptive robust interaction force controllers are presented both for the phase of double leg support and single leg support to effectively deal with multiple walking phases, negative effect of high-order nonlinearities of hydraulic systems as well as various model uncertainties and external disturbances. An additional torque allocation method is proposed to deal with the over-actuated characteristic in double leg support phase. Comparative simulations are carried out. The results demonstrate that the proposed force controller can achieve precision and strong robust interaction force control performance to load change and variation of the human trajectory.

Generalized Multiphase Dynamic Modeling and Precision Interaction Force Control of a Walking Lower Limb Hydraulic Exoskeleton.

Wearable lower limb hydraulic exoskeletons can be used to augment the human performance in heavy load transportation. Nonlinear and walking phase-dependent dynamics make the lower limb hydraulic exoskeleton become difficult to be modeled. This paper presents a generalized multiphase dynamic modeling method in which the dynamic model of each walking phase can all be solved based on a general higher dimensional dynamic model and different holonomic constraints. Compared to traditional lower limb exoskeleton modeling methods where the modeling of each walking phase is done independently, the proposed method is simple and applicable to arbitrary walking phases, especially for double leg support phase (closed-chain dynamics). Based on the established dynamic models, MIMO adaptive robust cascade force controllers (ARCFC) are designed both for double leg support phase and single leg support phase to effectively address high-order nonlinearities and various modeling uncertainties in hydraulic exoskeletons. An additional torque allocation method is proposed to deal with the overactuated characteristic in double leg support. Comparative simulations are conducted to verify the excellent human-machine interaction force control performance of the proposed scheme.

Precision force control of an underactuated stance leg exoskeleton for human performance augmentation

Lower limb exoskeleton which augments the human performance is a wearable human–machine integrated system used to assist people carrying heavy loads. Recently, underactuated lower limb exoskeleton systems with some passive joints become more and more attractive due to the advantages of smaller weight, lower system energy consumption and lower cost. However, because of the less of control inputs, the existed control methods of fully actuated exoskeletons cannot be extended to underactuated systems, which makes the robust controller design of underactuated lower limb exoskeletons becomes more challenged. This article focuses on the high-performance human–machine interaction force control design of underactuated lower limb exoskeletons with passive ankle joint. In order to solve the reduction of control inputs, the holonomic constraint from the wearer is considered, which help transform the dynamics of 3-degree-of-freedom underactuated exoskeleton in joint space into a 2-degree-of-freedom fully actuated system in Cartesian space. A two-level interaction force controller using adaptive robust control algorithm is proposed to effectively address the negative effect of various model uncertainties and external disturbances. In order to facilitate the control parameter selection, a gain tuning method is also presented. Comparative simulations are carried out, which indicate that the proposed two-level interaction force controller achieves smaller interaction force and better robust performance to various modeling errors and disturbances.

The Effect of Applying Robot-Assisted Task-Oriented Training Using Human-Robot Collaborative Interaction Force Control Technology on Upper Limb Function in Stroke Patients: Preliminary Findings.

Stroke is one of the leading causes of death and the primary cause of acquired disability worldwide. Many stroke survivors have difficulty using their upper limbs, which have important functional roles in the performance of daily life activities. Consequently, the independence and quality of life of most stroke patients are reduced. Robot-assisted therapy is an effective intervention for improving the upper limb function of individuals with stroke. Human-robot collaborative interaction force control technology is critical for improving the flexibility and followability of the robot's motion, thereby improving rehabilitation training outcomes. However, there are few reports on the effect of robot-assisted rehabilitative training on upper limb function. We applied this technology using a robot to assist patients with task-oriented training. Posttreatment changes in Fugl-Meyer and modified Barthel index (MBI) scores were assessed to determine whether this technology could improve the upper limb function of stroke patients. One healthy adult and five stroke patients, respectively, participated in functional and clinical experiments. The MBI and Fugl-Meyer scores of the five patients in the clinical experiments showed significant improvements after the intervention. The experimental results indicate that human-robot collaborative interaction force control technology is valuable for improving robots' properties and patients' recovery. This trial was registered in the Chinese clinical trial registry (ChiCTR2000038676).

Interactive Force Control Based on Multimodal Robot Skin for Physical Human−Robot Collaboration

This work proposes and realizes a control architecture that can support the deployment of a large‐scale robot skin in a Human‐Robot Collaboration scenario. It is shown, how whole‐body tactile feedback can extend the capabilities of robots during dynamic interactions by providing information about multiple contacts across the robot's surface. Specifically, an uncalibrated skin system is used to implement stable force control while simultaneously handling the multi‐contact interactions of a user. The system formulates control tasks for force control, tactile guidance, collision avoidance, and compliance, and fuses them with a multi‐priority redundancy resolution strategy. The approach is evaluated on an omnidirectional mobile‐manipulator with dual arms covered with robot skin. Results are assessed under dynamic conditions, showing that multi‐modal tactile information enables robust force control while at the same time remaining responsive to a user's interactions.

Precision Interaction Force Control of an Underactuated Hydraulic Stance Leg Exoskeleton Considering the Constraint from the Wearer

Hydraulic lower limb exoskeletons are wearable robotic systems, which can help people carry heavy loads. Recently, underactuated exoskeletons with some passive joints have been developed in large numbers for the purpose of decreasing the weight and energy consumption of the system. There are many control algorithms for a multi-joint fully actuated exoskeleton, which cannot be applied for underactuated systems due to the reduction in the number of control inputs. Besides, since the hydraulic actuator is not a desired force output source, there exist high order nonlinearities in hydraulic exoskeletons, which makes the controller design more challenging than motor driven exoskeleton systems. This paper proposed a precision interaction force controller for a 3DOF underactuated hydraulic stance leg exoskeleton. First, the control effect of the wearer is considered and the posture of the exoskeleton back is assumed as a desired trajectory under the control of the wearer. Under this assumption, the system dynamics are changed from a 3DOF underactuated system in joint space to a 2DOF fully actuated system in Cartesian space. Then, a three-level interaction force controller is designed in which the high-level controller conducts human motion intent inference, the middle level controller tracks human motion and the low-level controller achieves output force tracking of hydraulic cylinders. The MIMO adaptive robust control algorithm is applied in the controller design to effectively address the high order nonlinearities of the hydraulic system, multi-joint couplings and various model uncertainties. A gain tuning method is also provided to facilitate the controller gains selection for engineers. Comparative simulations are conducted, which demonstrate that the principal human-machine interaction force components can be minimized and good robust performance to load change and modeling errors can be achieved.

Endoluminal Motion Recognition of a Magnetically-Guided Capsule Endoscope Based on Capsule-Tissue Interaction Force.

A magnetically-guided capsule endoscope, embedding flexible force sensors, is designed to measure the capsule-tissue interaction force. The flexible force sensor is composed of eight force-sensitive elements surrounding the internal permanent magnet (IPM). The control of interaction force acting on the intestinal wall can reduce patient’s discomfort and maintain the magnetic coupling between the external permanent magnet (EPM) and the IPM during capsule navigation. A flexible force sensor can achieve this control. In particular, by analyzing the signals of the force sensitive elements, we propose a method to recognize the status of the motion of the magnetic capsule, and provide corresponding formulas to evaluate whether the magnetic capsule follows the motion of the external driving magnet. Accuracy of the motion recognition in Ex Vivo tests reached 94% when the EPM was translated along the longitudinal axis. In addition, a method is proposed to realign the EPM and the IPM before the loss of their magnetic coupling. Its translational error, rotational error, and runtime are 7.04 ± 0.71 mm, 3.13 ± 0.47, and 11.4 ± 0.39 s, respectively. Finally, a control strategy is proposed to prevent the magnetic capsule endoscope from losing control during the magnetically-guided capsule colonoscopy.

Active Interaction Force Control for Contact-Based Inspection With a Fully Actuated Aerial Vehicle

This article presents and validates active interaction force control and planning for fully actuated and omnidirectional aerial manipulation platforms, with the goal of aerial contact inspection in unstructured environments. We present a variable axis-selective impedance control which integrates direct force control for intentional interaction, using feedback from an on-board force sensor. The control approach aims to reject disturbances in free flight, while handling unintentional interaction and actively controlling desired interaction forces. A fully actuated and omnidirectional tilt-rotor aerial system is used to show capabilities of the control and planning methods. Experiments demonstrate disturbance rejection, push-and-slide interaction, and force-controlled interaction in different flight orientations. The system is validated as a tool for nondestructive testing of concrete infrastructure, and statistical results of interaction control performance are presented and discussed.

WeHAPTIC: A Wearable Haptic interface for Accurate Position Tracking and Interactive force Control

In this paper, a hand wearable system for virtual reality (VR), which is called as WeHAPTIC (Wearable Haptic interface for Accurate Position Tracking and Interactive force Control), is proposed. It shows improved performances in terms of finger motion measurement and force feedback compared with existing systems such as finger joint angle calculation and precise force control. Intensive experiments are also conducted to verify the mechanical performance as a haptic interface. The finger joint angles are inversely calculated by an estimation algorithm based on the fingertip positions with only one calibration posture. Not only the index finger motion which is generally measured, but also the thumb motion is accurately measured by our proposed algorithm. Accurate force feedback is achieved by robust control of the motor current. The motion measurement accuracy of average 2.62∘ errors in the thumb and index finger was verified by an infrared motion capture system. For performance evaluation of the force control, a Z-width and various interaction experiments were conducted; variable stiffness were generated, and various sizes & shapes of virtual objects were identified.

Physical human–robot interaction force control method based on adaptive variable impedance

This study presents a method for extracting the actual interaction force from a force sensor signal by measuring the gravity of the end-effector. The variable impedance control problem of commercial industrial robots is studied, to investigate the uncertain contact impedance characteristics between the human palm and a robot end-effector in the physical human-robot interaction process. A speed-based variable impedance adaptive interaction control method is proposed. The damping parameters of the admittance controller are adaptively adjusted according to the interactive force tracking error. The desired speed of the robot is also given, which is tracked in the Cartesian space speed mode of a Stäubli TX-90 robot to achieve interactive force control. An experimental comparison is made between the control strategies of the constant impedance and adaptive variable impedance. The experimental results show that the proposed method is effective.