Position-based Control Research Articles

Tele-Impedance control of a virtual avatar based on EMG and M-IMU sensors: a proof-of-concept

The broad spread of cooperative robots into many application domains has resulted in a demand for intuitive and effective solutions for teleoperated control. A relevant role in teleoperation has been assumed by impedance controllers, that allow the increase of stability and accuracy during interaction. This paper aims to test a teleoperation method based on an impedance controller, namely tele-impedance control, that is usable in unstructured environments since it relies only on wearable sensors. The proposed solution maps the joint stiffness and position of the human user, computed through six EMG and two M-IMU sensors, into the remote system to be teleoperated. We developed a 2-DoFs virtual task involving virtual physical interactions to compare the performance of our solution with the one of a traditional position-based controller. The study has been conducted on five healthy participants, who experienced both controllers in two different sessions. The tele-impedance approach has proved to be less physically demanding and more intuitive than the position-based one. Experimental data also allow us to investigate the strategy employed by the volunteers in the case of remote interactions, while using the two controllers. Of note, even though with the position controller the variation of subject impedance has no effect on the virtual arm, participants still tend to regulate both impedance and position of their own arm.

Embodied AI for dexterity-capable construction Robots: DEXBOT framework

The construction industry is integrating robots into critical tasks at an accelerated pace, with the aim of enhancing efficiency, safety, and productivity. However, construction tasks requiring dexterity remain a challenge due to the need for precise movements, accurate perception, real-time decision-making, and a comprehensive understanding of the environment. To address these challenges, the introduction of embodied artificial intelligence (AI) represents a significant shift in robotic capabilities to enhance their alignment with the broader spectrum of construction settings. Rooted in cognitive science, embodied AI emphasizes the integration of an agent’s physical form into its computational intelligence processes. It resembles how humans develop motor skills by interacting with physical world. This paper introduces DEXBOT, an exploratory framework for designing construction robots capable of high dexterity using embodied AI principles that mimics human strategies in complex tasks. The framework outlines six key perspectives for solving high-dexterity tasks with embodied AI: scene understanding, localization and motion planning, position-based control, force-based control, sequence planning, and correction decision-making. By presenting preliminary test cases for each perspective, the paper emphasizes the role of embodied AI in advancing dexterity level of construction robots. The DEXBOT framework is expected to encourage interdisciplinary collaboration of designing capable construction robots in the future.

Online Learning-Informed Feedforward-Feedback Controller Synthesis for Path Tracking of Autonomous Vehicles

High-performance path tracking is a key technology for autonomous vehicles. Feedforward-feedback control architectures are suitable for accurate path tracking with adequate margins of stability. For system modelling in the feedforward component, the learning-based method has been proven to be a promising approach owing to its model-free framework. However, the offline-learned data model trained with collection data is confined by its feature space, resulting in insufficient generalization. As a solution, in this study, we introduce an online learning network – the recurrent high-order neural network (RHONN) – to characterize vehicle behaviors. The RHONN is used to feature vehicle behaviors in a timely manner with a high fidelity and flexible form. The equilibrium at the preview point on the desired path is found based on the online-identified RHONN model, and its induced steering angle is taken as the feedforward command. For the feedback steering controller, the preview point position-based control law incorporating the steady vehicle sideslip angle is adopted to enhance the stability performance. Finally, in the CarSim/Simulink environment, the performance of the designed RHONN-informed feedforward and feedback controller is validated in two typical scenarios – double-lane change and single-turn. The validation results reveal that the proposed approach offers better tracking accuracy in linear and nonlinear regions than other techniques. More notably, the average execution time (3.55 ms) is less than the sampling frequency of the controller (50 ms), which further confirms the applicability and efficiency of the proposed approach.

Fast soft contact motion using force control with virtual viscosity field

Interactions between robots and their environment are important to perform a variety of tasks. During interactions, contact is primitive but essential because a robot should generally contact an object before performing a task. Before contact, the robot should approach an object and then establish contact with a target force. Thus, force control plays an important role during contact. However, an impulsive force may occur if a robot has a non-negligible velocity upon contact. Such impulsive force may damage the object or robot and thus should be prevented. We propose a force control law using a virtual viscosity field. The viscosity coefficient in the proposed force control law changes according to the relative position between the robot and object. As a result, the proposed control law simultaneously enables fast approaching and the reduction of impulsive forces upon contact without requiring a motion planning algorithm or controller switching. In addition, the proposed control law provides robustness to position errors compared with conventional position-based control. Experimental results validate the proposed method on a two degree-of-freedom manipulator.

Experimental Validation of Torque-Based Control for Realistic Handwheel Haptics in Driving Simulators

A realistic steering feel is one of the key elements to guarantee fidelity on a driving simulator in general, and in particular to replicate on-centre vehicle handling. This requires precise modelling of the steering dynamics, a high bandwidth control loading system, and coupling of virtual and physical components in agreement with computational requirements and hardware limitations. For such a coupling, the common approach position-based control uses the measured signals of steering wheel angle and steering rate as inputs to the steering system model. This paper proposes an alternative torque-based control scheme using steering torque as an input to the steering system with additional compensation. Torque-based control was designed and evaluated in conjunction with detailed electric power steering models including state-of-the-art friction models, a neuromuscular driver model, and two driver-in-the-loop experiments in a high-end driving simulator. The objective analysis performed by means of the neuromuscular driver model reveals that the driver applies less impedance i.e. the driver is less stiff on a driving simulator when steering feedback is provided with the torque-based control compared to the position-based control. The investigations demonstrate that torque-based control reduces haptic response delay and vibrations caused by friction modelling compared to position-based control. The driver-in-the-loop experiments show significant objective effects on steering performance and subjective evaluation of fidelity and effort. We conclude that the proposed approach closes the vehicle-driver loop with more realism in a driving simulator.

Localization and Control of Magnetic Suture Needles in Cluttered Surgical Site with Blood and Tissue.

Real-time visual localization of needles is necessary for various surgical applications, including surgical automation and visual feedback. In this study we investigate localization and autonomous robotic control of needles in the context of our magneto-suturing system. Our system holds the potential for surgical manipulation with the benefit of minimal invasiveness and reduced patient side effects. However, the nonlinear magnetic fields produce unintuitive forces and demand delicate position-based control that exceeds the capabilities of direct human manipulation. This makes automatic needle localization a necessity. Our localization method combines neural network-based segmentation and classical techniques, and we are able to consistently locate our needle with 0.73 mm RMS error in clean environments and 2.72 mm RMS error in challenging environments with blood and occlusion. The average localization RMS error is 2.16 mm for all environments we used in the experiments. We combine this localization method with our closed-loop feedback control system to demonstrate the further applicability of localization to autonomous control. Our needle is able to follow a running suture path in (1) no blood, no tissue; (2) heavy blood, no tissue; (3) no blood, with tissue; and (4) heavy blood, with tissue environments. The tip position tracking error ranges from 2.6 mm to 3.7 mm RMS, opening the door towards autonomous suturing tasks.

Structure-Controlled Variable Stiffness Robotic Joint Based on Multiple Rotary Flexure Hinges

Variable stiffness actuator (VSA) plays an important role for achieving safety and robustness for robots to physically interact with environments. This article proposes a structure-controlled approach in developing a new variable stiffness robotic joint with the merits of compactness, high stiffness, and high displacement-to-stiffness ratio. Unlike the common methods of using antagonist springs and lever concept, the structure-controlled approach achieves stiffness variation through the adjustment of mechanical structure configuration inside the actuator. Specifically, the stiffness variation is realized by changing the effective second moment of area through rotating a combination of leaf flexure hinges arranged in a radial configuration. A range of configurations are studied and the configuration we identified has the advantages of minimum unbalanced twists in the mechanism while providing high transmission torque, large stiffness, range and minimum lateral buckling effects. The performance characteristics are accomplished through a novel variable stiffness mechanism and a position-based control strategy for both position and torque control modes. A prototype system was implemented to verify the characteristic and performance of the design concept. It demonstrated that the new VSA system is effective for realising independent joint-stiffness and joint-position control of robot joints.

Mechanisms used to increase propulsive forces on a treadmill in older adults

Older adults typically demonstrate reductions in overground walking speeds and propulsive forces compared to young adults. These reductions in walking speeds are risk factors for negative health outcomes. Therefore, this study aimed to determine the effect of an adaptive speed treadmill controller on walking speed and propulsive forces in older adults, including the mechanisms and strategies underlying any change in propulsive force between conditions. Seventeen participants completed two treadmill conditions, one with a fixed comfortable walking speed and one with an adaptive speed controller. The adaptive speed treadmill controller utilized a set of inertial-force, gait parameters, and position-based controllers that respond to an instantaneous anterior inertial force. A biomechanical-based model previously developed for individuals post-stroke was implemented for older adults to determine the primary gait parameters that contributed to the change in propulsive forces when increasing speed. Participants walked at faster average speeds during the adaptive speed controller (1.20 m/s) compared to the fixed speed controller conditions (0.98 m/s); however, these speeds were not as fast as their overground speed (1.44 m/s). Although average trailing limb angle (TLA) (p < 0.001) and ankle moment (p = 0.020) increased when speed also increased between treadmill conditions, increasing TLA contributed more to the increased propulsive forces seen during faster treadmill speeds. Our findings show that older adults chose faster walking speeds and increased propulsive force when walking on an adaptive speed treadmill compared to a fixed speed treadmill, suggesting that an adaptive speed treadmill controller has the potential to be a beneficial alternative to current exercise interventions for older adults.

Feasibility of Two Different EMG-Based Pattern Recognition Control Paradigms to Control a Robot After Stroke - Case Study.

Stroke often results in chronic motor impairment of the upper-extremity yet neither traditional- nor robotics-based therapy has been able to affect this in a profound way. Supporting the weak affected shoulder against gravity improves reaching distance and minimizes abnormal co-contraction of the elbow, wrist, and fingers after stroke. However, it is necessary to assess the feasibility and efficacy of real-time controllers for this population as technology advances and a wearable shoulder device comes closer to reality. The aim of this study is to test two EMG-based controllers in this regard. A linear discriminant analysis based classifier was trained using extracted time domain and auto-regressive features from electromyographic data acquired during muscle effort required to move a load equivalent to 50 and 100% limb weight (abduction) and 150 and 200% limb weight (adduction). While rigidly connected to a custom lab-based robot, the participant was required to complete a series of lift and reach tasks under two different control paradigms: position-based control and force-based control. The participant successfully controlled the robot under both paradigms as indicated by first moving the robot arm into the proper vertical window and then reaching out as far as possible while remaining within the vertical window. This case study begins to assess the feasibility of using electromyographic data to classify the intended shoulder movement of a participant with stroke during a functional lift and reach type task. Next steps will assess how this type of support affects reaching function.

PUT-Hand—Hybrid Industrial and Biomimetic Gripper for Elastic Object Manipulation

In this article, the design of a five-fingered anthropomorphic gripper is presented specifically designed for the manipulation of elastic objects. The manipulator features a hybrid design, being equipped with three fully actuated fingers for precise manipulation, and two underactuated, tendon-driven digits for secure power grasping. For ease of reproducibility, the design uses as many off-the-shelf and 3D-printed components as possible. The on-board controller circuit and firmware are also presented. The design includes resistive position and angle sensors in each joint, resulting in full joint observability. The controller has a position-based controller integrated, along with USB communication protocol, enabling gripper state reporting and direct motor control from a PC. A high-level driver operating as a Robot Operating System node is also provided. All drives and circuitry of the PUT-Hand are integrated within the hand itself. The sensory system of the hand includes tri-axial optical force sensors placed on fully actuated fingers’ fingertips for reaction force measurement. A set of experiments is provided to present the motion and perception capabilities of the gripper. All design files and source codes are available online under CC BY-NC 4.0 and MIT licenses.

Experimental capabilities and limitations of a position-based control algorithm for swarm robotics

Achieving efficient and reliable self-organization in groups of autonomous robots is a fundamental challenge in swarm robotics. Even simple states of collective motion, such as group translation or rotation, require nontrivial algorithms, sensors, and actuators to be achieved in real-world scenarios. We study here the capabilities and limitations in controlling experimental robot swarms of a decentralized control algorithm that only requires information on the positions of neighboring agents, and not on their headings. Using swarms of e-Puck robots, we implement this algorithm in experiments and show its ability to converge to self-organized collective translation or rotation, starting from a state with random orientations. Through a simple analytical calculation, we also unveil an essential limitation of the algorithm that produces small persistent oscillations of the aligned state, related to its marginal stability. By comparing predictions and measurements, we compute the experimental noise distributions of the linear and angular robot speeds, showing that they are well described by Gaussian functions. We then implement simulations that model this noise by adding Gaussian random variables with the experimentally measured standard deviations. These simulations are performed for multiple parameter combinations and compared to experiments, showing that they provide good predictions for the expected speed and robustness of the self-organizing dynamics.

Force Control of In-Wheel-Motored Electric Vehicles

Electric vehicles (EVs) are gaining attention, and novel use of cars such as vehicle-to-home is becoming widespread. In particular, in-wheel-motor (IWM) type EVs, which have four motors inside each wheel, are expected to be an ideal power train system because it can achieve precise driving force control and estimation. To exploit these features of IWM-EVs, researchers have proposed numerous control methods, most of which are velocity or position-based control for running or trajectory tracking. Force control technologies are quite popular in robotics divisions, and these methods are quite suitable for the new applications of EVs. By taking advantage of the precise estimation performance of IWM-EVs, the authors have proposed to apply force control to EVs and built the concept of human-friendly EVs as done in the robotics division. We show the external force estimation method using wheel resolvers and propose hand-assisted position adjustment method based on impedance control. The appropriate control structure is discussed for IWM-EVs, and its effectiveness is demonstrated with numerical simulations and experiments. We anticipate this research to generate many control applications suitable for the new application of cars.

Real-time freeform surface and path tracking for force controlled robotic tooling applications

Abstract Modern force controlled robotic manipulators in combination with additional sensor configurations allow a wide range of operational scenarios for flexible production processes. This paper considers a sensor-based geometric approach for tracking curved freeform surfaces with the focus on robotic tooling applications such as polishing and grinding. A local approximation of the surface geometry is created from sparse distance measurements between the robotic tool and the workpiece maintaining a desired distance and orientation relative to the surface. The approach only relies on current sensor data and does not require any a priori model of the geometry from CAD data or previous 3D scans of the workpiece. The considered surface tracking controller allows the additional tracking of a planned path. The presented control is extended by a force controller to achieve a desired interaction force between the manipulator and the workpiece at a specified angle. To provide a flexible automation solution for the tooling of freeform surfaces, a path planning method is presented to cover a larger area based on the teaching of the area’s boundary. The considered approaches are realized on an experimental setup with the robotic manipulator KUKA LBR iiwa 14, where the surface tracking capabilities, as well as the performance of the force controller, are evaluated. The surface tracking and force controller are implemented directly on the robot controller while the sensor data processing is performed on a PLC which is connected to the robot controller via EtherCAT. The setup uses the position-based control interface for the integration of the surface tracking and the force control which introduces some limits on the performance of the applied control schemes but preserves the safety features and the collaborative behavior of the robotic manipulator.

Development of a Novel Robotic Rehabilitation System With Muscle-to-Muscle Interface.

In this study, we developed a novel robotic system with a muscle-to-muscle interface to enhance rehabilitation of post-stroke patients. The developed robotic rehabilitation system was designed to provide patients with stage appropriate physical rehabilitation exercise and muscular stimulation. Unlike the position-based control of conventional bimanual robotic therapies, the developed system stimulates the activities of the target muscles, as well as the joint movements of the paretic limb. The robot-assisted motion and the electrical stimulation on the muscles of the paretic side are controlled by on-line comparison of the motion and the muscle activities between the paretic and unaffected sides. With the developed system, the rehabilitation exercise can be customized and modulated depending on the patient’s stage of motor recovery after stroke. The system can be operated in three different modes allowing both passive and active exercises. The effectiveness of the developed system was verified with healthy human subjects, where the subjects were paired to serve as the unaffected side and the paretic side of a hemiplegic patient.

Compliance Control and Analysis for Equivalent Hydraulic Legs

In this article, the compliant behavior of an equivalent hydraulic leg is achieved by combining active compliance control of hydraulic cylinder with a passive spring at its end effector. Firstly, a position-based active compliance control is proposed based on impedance control to reduce the contact impact. Secondly, the stability of proposed compliance controller and the range of impedances (Z-width) of compliance control are analyzed, and the design procedure of proposed compliance controller is given out. Finally, experimental results show that the contact impacts could be well handled by the proposed control method. This research provides an insight for the compliance control of hydraulic legged robots.

Design and Implementation of Position-Based Repetitive Control Torque Observer for Cogging Torque Compensation in PMSM

In permanent magnet machines, the cogging torque caused by reluctance variations in the air gap may degrade the speed control performance in low speed and will undoubtedly limit its operational range. In order to reduce the cogging torque, this paper proposes a position-based repetitive control observer aiming at cogging torque estimation and further rejection. This new scheme of observer design possesses the capability of repeatedly learning the observed clogging torque in each rotation to achieve higher estimation accuracy. An online/offline feedforward compensation strategy that employs the forgetting factor principle and position-based average generates the cogging torque compensation lookup table learned from the position-based repetitive control observer. To verify the overall control performance of the proposed observed design technique, a hardware in the loop control device is employed, and then an experimental setup with a permanent magnet synchronous motor and its power drive was adopted.

Fringe-Projection-Based Normal Direction Measurement and Adjustment for Robotic Drilling

In a large-scale structure assembly, a highly accurate normal direction measurement for robotic drilling is required. However, the robustness and accuracy of the existing normal direction measurement method with a range sensor would dramatically decrease for a high-curvature surface, such as the surface edge, due to the low resolution of the range sensor. To this end, a high-resolution fringe projection sensor is proposed to improve the measurement robustness and accuracy. In this method, different from the control scheme design with a reconstructed three-dimensional point cloud in fringe projection profilometry, which involves a high computational complexity, a novel two-dimensional phase-map-based two-stage control scheme, including coarse and refined normal direction adjustments, is designed to improve the robustness and accuracy. The coarse normal direction adjustment uses a phase map to estimate the normal direction, which is used to synthesize a position-based control scheme, leading to high robustness; the refined normal direction adjustment uses a phase map to synthesize an image-based control scheme, resulting in high accuracy. The proposed method is validated by simulations and experiments, which indicate that the proposed method is effective.

Stable, three degree-of-freedom myoelectric prosthetic control via chronic bipolar intramuscular electrodes: a case study

BackgroundModern prosthetic hands are typically controlled using skin surface electromyographic signals (EMG) from remaining muscles in the residual limb. However, surface electrode performance is limited by changes in skin impedance over time, day-to-day variations in electrode placement, and relative motion between the electrodes and underlying muscles during movement: these limitations require frequent retraining of controllers. In the presented study, we used chronically implanted intramuscular electrodes to minimize these effects and thus create a more robust prosthetic controller.MethodsA study participant with a transradial amputation was chronically implanted with 8 intramuscular EMG electrodes. A K Nearest Neighbor (KNN) regression velocity controller was trained to predict intended joint movement direction using EMG data collected during a single training session. The resulting KNN was evaluated over 12 weeks and in multiple arm posture configurations, with the participant controlling a 3 Degree-of-Freedom (DOF) virtual reality (VR) hand to match target VR hand postures. The performance of this EMG-based controller was compared to a position-based controller that used movement measured from the participant’s opposite (intact) hand. Surface EMG was also collected for signal quality comparisons.ResultsSignals from the implanted intramuscular electrodes exhibited less crosstalk between the various channels and had a higher Signal-to-Noise Ratio than surface electrode signals. The performance of the intramuscular EMG-based KNN controller in the VR control task showed no degradation over time, and was stable over the 6 different arm postures. Both the EMG-based KNN controller and the intact hand-based controller had 100% hand posture matching success rates, but the intact hand-based controller was slightly superior in regards to speed (trial time used) and directness of the VR hand control (path efficiency).ConclusionsChronically implanted intramuscular electrodes provide negligible crosstalk, high SNR, and substantial VR control performance, including the ability to use a fixed controller over 12 weeks and under different arm positions. This approach can thus be a highly effective platform for advanced, multi-DOF prosthetic control.

Position-Based Control of Under-Constrained Haptics: A System for the Dexmo Glove

The Dexmo glove is a haptic exoskeleton that provides kinesthetic feedback in virtual reality. Unlike many other gloves based on string–pulleys, the Dexmo uses a free-hinged link-bar to transfer forces from a crank to the fingertips. It also uses an admittance-based controller parameterized by position, as opposed to an impedance-based controller parameterized by force. When setting the controller’s target position, developers must use its native angular coordinate system. The Dexmo has a number of uninstrumented degrees of freedom. Mature forward models can reliably predict the hand pose, even with these unknowns. When it comes to computing angular controller parameters from a target pose in Cartesian space however, things become more difficult. Complex models that provide attractive visuals from a small number of sensors can be non-trivial or even impossible to invert. In this letter, we suggest side-stepping this issue. We sample the forward model in order to build a lookup table. This is embedded in three-dimensional space as a curve, on which traditional queries against world geometry can be performed. Controller parameters are stored as attributes of the sample points. To compute the driver parameters for a target position, the application constrains the position to the geometry, and interpolates them. This technique is generalizable, stable, simple, and fast. We validate our approach by implementing it in Unity 2017.3 and integrating it with a Dexmo glove.

Position-Based Synchronization of Networked Harmonic Oscillators With Asynchronous Sampling and Communication Delays.

This paper is concerned with position-based synchronization of networked harmonic oscillators. Note that synchronization cannot be achieved via current-position-based protocols. The objective of this paper is to investigate the positive effects of network-induced delays on the synchronization of networked harmonic oscillators. That is, if taking network-induced delays into account, the motion of harmonic oscillators can be really synchronized via a proper position-based control protocol. In doing so, the harmonic oscillators are connected via a shared digital communication network. Different from some existing results, system measurements from oscillator nodes are sampled in an asynchronous way; and network-induced delays are assumed to be time varying and bounded, and they do not need to be synchronous with those from the other communication channels. At each oscillator node, a buffer is embedded into the controller to store the newest sampled-data packets transmitted from its neighboring nodes through communication channels. Then, based on the store of the buffer, the controller computes its control signal with its own period. As a result, the overall synchronization error system is modeled as a linear system with multiple interval time-varying delays. By employing the discretized Lyapunov-Krasovskii functional method, a sufficient condition on synchronization of networked harmonic oscillators is derived, which can ensure that the synchronization error system is asymptotically stable for network-induced delays falling into a certain closed interval whose lower bound is a positive real number. This condition is thus used to design suitable control protocols in terms of linear matrix inequalities with several tuning parameters. Finally, a multirobot platform is given to demonstrate the effectiveness of the proposed method.