Physical Human-robot Interaction Research Articles

Safe physical human–robot interaction through variable impedance control based on ISO/TS 15066

Safe physical human–robot interaction through variable impedance control based on ISO/TS 15066

Repetitive Impedance Learning-Based Physically Human-Robot Interactive Control.

Model-based impedance learning control can provide variable impedance regulation for robots through online impedance learning without interaction force sensing. However, the existing related results only guarantee the closed-loop control systems to be uniformly ultimately bounded (UUB) and require the human impedance profiles being periodic, iteration-dependent, or slowly varying. In this article, a repetitive impedance learning control approach is proposed for physical human-robot interaction (PHRI) in repetitive tasks. The proposed control is composed of a proportional-differential (PD) control term, an adaptive control term, and a repetitive impedance learning term. Differential adaptation with projection modification is designed for estimating robotic parameters uncertainties in the time domain, while fully saturated repetitive learning is proposed for estimating time-varying human impedance uncertainties in the iterative domain. Uniform convergence of tracking errors is guaranteed by the PD control and the use of projection and full saturation in the uncertainties estimation and is theoretically proved based on a Lyapunov-like analysis. In impedance profiles, the stiffness and damping are composed of an iteration-independent term and an iteration- dependent disturbance, which are estimated by repetitive learning and compressed by the PD control, respectively. Therefore, the developed approach can be applied to the PHRI where iteration-dependent disturbances exist in the stiffness and damping. The control effectiveness and advantages are validated by simulations on a parallel robot in a repetitive following task.

Sensorless robot collision detection based on fuzzy momentum observer

During physical human–robot interaction, unwanted collisions occur, which also threaten human safety. That is why different collision detection algorithms are proposed for safe collaboration between human and robots. The current detection methods are model-based methods without using additional sensors. However, the estimation of the collision force using a model-based method still has the problem of an unavoidable trade-off between the collision sensitivity and the reduction of the peaking value, and, in addition, the immunity to the model uncertainties. In this paper, an improved momentum observer using fuzzy system is proposed for robot collision detection. First, to avoid using the robot’s inverse inertia matrix, the generalized moment state equations based on the robot’s dynamics are deduced. Second, the extended linear momentum observer is built based on robot generalized momentum equations. Third, a fuzzy system is designed to select an appropriate bandwidth for the observer. In the design of this fuzzy system, the estimation error is considered as a fuzzy input, and the observer’s bandwidth is considered as its output. This fuzzy generalized momentum observer (FGMO) is compared with the existing generalized moment observers (GMOs) and has been proven its ability to overcome the mentioned above issues due to its ability to intelligently adjust the online filter parameters during the observation process. Finally, simulations are conducted with a 6-degree-of-freedom (6-DOF) industrial robot manipulator, and the obtained results illustrate the effectiveness of proposed approach in term of sensitivity and peaking value.

Inferring Human Control Intent Using Inverse Linear Quadratic Regulator With Output Penalty Versus Gain Penalty: Better Fit but Similar Intent

Abstract To identify the underlying mechanisms of human motor control, parametric models are utilized. One approach of employing these models is the inferring the control intent (estimating motor control strategy). A well-accepted assumption is that human motor control is optimal; thus, the intent is inferred by solving an inverse optimal control (IOC) problem. Linear quadratic regulator (LQR) is a well-established optimal controller, and its inverse LQR (ILQR) problem has been used in the literature to infer the control intent of one subject. This implementation used a cost function with gain penalty, minimizing the error between LQR gain and a preliminary estimated gain. We hypothesize that relying on an estimated gain may limit ILQR optimization capability. In this study, we derive an ILQR optimization with output penalty, minimizing the error between the model output and the measured output. We conducted the test on 30 healthy subjects who sat on a robotic seat capable of rotation. The task utilized a physical human–robot interaction with a perturbation torque as input and lower and upper body angles as output. Our method significantly improved the goodness of fit compared to the gain-penalty ILQR. Moreover, the dominant inferred intent was not statistically different between the two methods. To our knowledge, this work is the first that infers motor control intent for a sample of healthy subjects. This is a step closer to investigating control intent differences between healthy subjects and subjects with altered motor control, e.g., low back pain.

Charting User Experience in Physical Human–Robot Interaction

Robots increasingly interact with humans through touch, where people are touching or being touched by robots. Yet, little is known about how such interactions shape a user’s experience. To inform future work in this area, we conduct a systematic review of 44 studies on physical human–robot interaction (pHRI). Our review examines the parameters of the touch (e.g., the role of touch, location), the experimental variations used by researchers, and the methods used to assess user experience. We identify five facets of user experience metrics from the questionnaire items and data recordings for pHRI studies. We highlight gaps and methodological issues in studying pHRI and compare user evaluation trends with the Human–Computer Interaction (HCI) literature. Based on the review, we propose a conceptual model of the pHRI experience. The model highlights the components of such touch experiences to guide the design and evaluation of physical interactions with robots and inform future user experience questionnaire development.

Comparative Analysis of Peltier Devices and Flexible Heater Strips for Enhancing Bandwidth in Thermo-Active Soft Actuators

Soft actuators are a new generation of robotic actuators designed for safer and more adaptable physical human-robot interaction, that can be triggered by various stimulating mechanisms, including pneumatic, electric, electromagnetic, light, magnetic, and thermal sources. Among the different types of soft actuators, thermoresponsive ones that utilize heat as the stimulus show great potential due to their ability to deliver a relatively high force-to-size ratio without the need for external air pumps, tethers, high voltage sources, or complex designs. However, a major drawback of such actuators is their limited bandwidth. Traditional methods rely on Joule heating for actuation, with the actuator deflating when the heat source is turned off and ambient temperature takes over. Recently, the Peltier mechanism has been introduced as an alternative approach for active heating and cooling. This research paper presents a comparative analysis of the Peltier and flexible heater mechanisms in terms of the bandwidth and energy consumption of phase-change thermo-active soft actuators. The study aims to assess the potential of Peltier-based actuation in addressing the bandwidth limitations observed in traditional soft actuators. The findings reveal that Peltier-based actuation can significantly improve actuation speed in thermoresponsive soft actuators. However, it is important to note that the performance of Peltier-based actuators decreases after a few cycles unless additional measures, such as the use of an external fan, are implemented. This increase in performance comes at the cost of higher energy consumption, which should be carefully considered in practical applications.

Arbitrary Surface Contact Sensing Method for Physical Human–Robot Interaction

Arbitrary Surface Contact Sensing Method for Physical Human–Robot Interaction

Human–Robot Cooperation Control Strategy Design Based on Trajectory Deformation Algorithm and Dynamic Movement Primitives for Lower Limb Rehabilitation Robots

Compliant physical interactions, interactive learning, and robust position control are crucial to improving the effectiveness and safety of rehabilitation robots. This paper proposes a human–robot cooperation control strategy (HRCCS) for lower limb rehabilitation robots. The high-level trajectory planner of the HRCCS consists of a trajectory generator, a trajectory learner, a desired trajectory predictor, and a soft saturation function. The trajectory planner can predict and generate a smooth desired trajectory through physical human–robot interaction (pHRI) in a restricted joint space and can learn the desired trajectory using the locally weighted regression method. Moreover, a triple-step controller was designed to be the low-level position controller of the HRCCS to ensure that each joint tracks the desired trajectory. A nonlinear disturbance observer is used to observe and compensate for total disturbances. The radial basis function neural networks (RBFNN) approximation law and robust term are adopted to compensate for observation errors. The simulation results indicate that the HRCCS is robust and can achieve compliant pHRI and interactive trajectory learning. Therefore, the HRCCS has the potential to be used in rehabilitation robots and other fields involving pHRI.

Synergistic Functional Muscle Networks Reveal the Passivity Behavior of the Upper-Limb in Physical Human-Robot Interaction

Synergistic Functional Muscle Networks Reveal the Passivity Behavior of the Upper-Limb in Physical Human-Robot Interaction

Physical Human–Robot Interaction Control of an Upper Limb Exoskeleton With a Decentralized Neuroadaptive Control Scheme

Physical Human–Robot Interaction Control of an Upper Limb Exoskeleton With a Decentralized Neuroadaptive Control Scheme

Improving Speed Characteristics of High-Torque-Density Motors for Physical Human–Robot Interaction Using an Independent Three-Phase Winding Structure

Recently, due to the decrease in labor force, increase in labor costs, and the desire for improved quality of life, research on robots has been actively conducted to address these issues. However, it is currently difficult to find robots that physically interact with humans. The reason is that the actuators of robots do not have a high torque density on their own. To solve this problem, high-torque-density motors, such as proprioceptive actuators, are being researched. However, the torque density is still insufficient for physical interaction with humans, so a motor with higher torque density has been developed. However, high-torque-density motors have the disadvantage of lower speed characteristics due to increased Back EMF levels. Therefore, to address the deterioration of speed characteristics in the developed motor, we applied the independent three-phase winding structure to improve the speed characteristics. Consequently, through comparison with the Y-Connection and D-Connection, we propose the most suitable winding structure for high-torque-density motors intended for physical interaction with humans.

Passive Model-Predictive Impedance Control for Safe Physical Human–Robot Interaction

Various cognitive systems have been designed to model the position and stiffness profiles of human behavior and then to drive robots by mimicking the human’s behavior to accomplish physical human-robot interaction tasks through a properly designed impedance controller. However, some studies have shown that variable stiffness parameters of the impedance controller can cause the violation of the passivity constraint of the robot states, and make the robot’s stored energy exceed the external energy injected from the human user, thus leading to the unsafe human-robot interaction. To solve this problem, this paper proposes a novel passive model predictive impedance control method including two control loops. In the bottom-loop of the proposed controller, the robot is driven by a variable impedance controller to achieve the desired compliant interaction behavior. In the top-loop of the proposed controller, the model predictive control (MPC) is used to ensure that the robot states satisfy the passivity constraint by calculating a complementary torque to limit the stored energy of the robot. The passivity of the closed-loop robot system and the feasibility of MPC are guaranteed by theoretical analysis, ensuring the safety of the robotic movement in the human-robot interaction. The effectiveness of the proposed method is demonstrated by the simulation and experiment on the Franka Emika Panda robot.

Decentralized approximated optimal control for modular robot manipulations with physical human–robot interaction: a cooperative game-based strategy

Decentralized approximated optimal control for modular robot manipulations with physical human–robot interaction: a cooperative game-based strategy

Human Understanding and Perception of Unanticipated Robot Action in the Context of Physical Interaction

Anticipating a future scenario where the robot initiates its own actions and behaves voluntarily when collaborating with humans, our research focuses on human understanding and perception of unanticipated robot actions during physical human-robot interaction. While the current literature searches for key factors that make the human-robot collaboration successful, the question of how people experience the robot’s unanticipated action as cooperative or uncooperative seems to remain open. We designed a game-based experiment (N = 35) where the participant played a “catch-falling-coins” game by moving a robotic arm. Our experiment introduced unanticipated robot actions in an “active session” where the robot targeted higher-valued coins without first informing the participants. Through semi-structured interviews and statistical analysis of questionnaires (Big Five Personality Test, SAM, NARS and CH33), we examined the participants’ understanding of the robot’s “intention” and their positive or negative perception of the robot as cooperative or uncooperative. Among the participants who understood that the robot’s “intention” was to catch the higher-valued coins, the majority of them reported a positive perception of the robot (cooperative or helpful) while this was not the case among those who did not understand the robot’s intention. We also observed relevant relationships between some personality traits and a person’s understanding of the robot’s intention. Qualitative analysis of the interviews allowed us to structure the process of perception change during the game into three phases: confusion, investigation, and adaptation. We believe that our research contributes to the study of human perception, and particularly to the relationship between a human’s understanding of unanticipated robot actions and their positive or negative perception of the robot.

Compliance while resisting: A shear-thickening fluid controller for physical human-robot interaction

Physical human-robot interaction (pHRI) is widely needed in many fields, such as industrial manipulation, home services, and medical rehabilitation, and puts higher demands on the safety of robots. Due to the uncertainty of the working environment, the pHRI may receive unexpected impact interference, which affects the safety and smoothness of the task execution. The commonly used linear admittance control (L-AC) can cope well with high-frequency small-amplitude noise, but for medium-frequency high-intensity impact, the effect is not as good. Inspired by the solid-liquid phase change nature of shear-thickening fluid, we propose a shear-thickening fluid control (SFC) that can achieve both an easy human-robot collaboration and resistance to impact interference. The SFC’s stability, passivity, and phase trajectory are analyzed in detail, the frequency and time domain properties are quantified, and parameter constraints in discrete control and coupled stability conditions are provided. We conducted simulations to compare the frequency and time domain characteristics of L-AC, nonlinear admittance controller (N-AC), and SFC and validated their dynamic properties. In real-world experiments, we compared the performance of L-AC, N-AC, and SFC in both fixed and mobile manipulators. L-AC exhibits weak resistance to impact. N-AC can resist moderate impacts but not high-intensity ones and may exhibit self-excited oscillations. In contrast, SFC demonstrated superior impact resistance and maintained stable collaboration, enhancing comfort in cooperative water delivery tasks. Additionally, a case study was conducted in a factory setting, further affirming the SFC’s capability in facilitating human-robot collaborative manipulation and underscoring its potential in industrial applications.

Experimental and Simulation-Based Estimation of Interface Power During Physical Human-Robot Interaction in Hand Exoskeletons

Experimental and Simulation-Based Estimation of Interface Power During Physical Human-Robot Interaction in Hand Exoskeletons

Whole-Body Intuitive Physical Human-Robot Interaction With Flexible Robots Using Non-Collocated Proprioceptive Sensing

Whole-Body Intuitive Physical Human-Robot Interaction With Flexible Robots Using Non-Collocated Proprioceptive Sensing

Decentralized variable impedance control of modular robot manipulators with physical human–robot interaction using Gaussian process-based motion intention estimation

Decentralized variable impedance control of modular robot manipulators with physical human–robot interaction using Gaussian process-based motion intention estimation

Low-force human–human hand interactions induce gait changes through sensorimotor engagement instead of direct mechanical effects

Physical human–robot interactions (pHRI) often provide mechanical force and power to aid walking without requiring voluntary effort from the human. Alternatively, principles of physical human–human interactions (pHHI) can inspire pHRI that aids walking by engaging human sensorimotor processes. We hypothesize that low-force pHHI can intuitively induce a person to alter their walking through haptic communication. In our experiment, an expert partner dancer influenced novice participants to alter step frequency solely through hand interactions. Without prior instruction, training, or knowledge of the expert’s goal, novices decreased step frequency 29% and increased step frequency 18% based on low forces (< 20 N) at the hand. Power transfer at the hands was 3–700 × smaller than what is necessary to propel locomotion, suggesting that hand interactions did not mechanically constrain the novice’s gait. Instead, the sign/direction of hand forces and power may communicate information about how to alter walking. Finally, the expert modulated her arm effective dynamics to match that of each novice, suggesting a bidirectional haptic communication strategy for pHRI that adapts to the human. Our results provide a framework for developing pHRI at the hand that may be applicable to assistive technology and physical rehabilitation, human-robot manufacturing, physical education, and recreation.

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.