Fractional order admittance control for physical human-robot interaction

In physical human-robot interaction (pHRI), the cognitive skill of a human is combined with the accuracy, repeatability and strength of a robot. While the promises and potential outcomes of pHRI are glamorous, the control of such coupled systems is challenging in many aspects. In this paper, we propose a new controller, fractional order admittance controller, for pHRI systems. The stability analysis of the new control system with human in-the-loop is performed and the interaction performance is investigated experimentally with 10 subjects during a task imitating a contact with a stiff environment. The results show that the fractional order controller is more robust than the standard admittance controller and helps to reduce the human effort in task execution

Adaptive human force scaling via admittance control for physical human-robot interaction

The goal of this article is to design an admittance controller for a robot to adaptively change its contribution to a collaborative manipulation task executed with a human partner to improve the task performance. This has been achieved by adaptive scaling of human force based on her/his movement intention while paying attention to the requirements of different task phases. In our approach, movement intentions of human are estimated from measured human force and velocity of manipulated object, and converted to a quantitative value using a fuzzy logic scheme. This value is then utilized as a variable gain in an admittance controller to adaptively adjust the contribution of robot to the task without changing the admittance time constant. We demonstrate the benefits of the proposed approach by a pHRI experiment utilizing Fitts’ reaching movement task. The results of the experiment show that there is a) an optimum admittance time constant maximizing the human force amplification and b) a desirable admittance gain profile which leads to a more effective co-manipulation in terms of overall task performance.WOS:000731146900006Scopus - Affiliation ID: 60105072Q2ArticleUluslararası işbirliği ile yapılan - EVETOctober2021YÖK - 2021-22Eki

A variable-fractional order admittance controller for pHRI

In today’s automation driven manufacturing environments, emerging technologies like cobots (collaborative robots) and augmented reality interfaces can help integrating humans into the production workflow to benefit from their adaptability and cognitive skills. In such settings, humans are expected to work with robots side by side and physically interact with them. However, the trade-off between stability and transparency is a core challenge in the presence of physical human robot interaction (pHRI). While stability is of utmost importance for safety, transparency is required for fully exploiting the precision and ability of robots in handling labor intensive tasks. In this work, we propose a new variable admittance controller based on fractional order control to handle this trade-off more effectively. We compared the performance of fractional order variable admittance controller with a classical admittance controller with fixed parameters as a baseline and an integer order variable admittance controller during a realistic drilling task. Our comparisons indicate that the proposed controller led to a more transparent interaction compared to the other controllers without sacrificing the stability. We also demonstrate a use case for an augmented reality (AR) headset which can augment human sensory capabilities for reaching a certain drilling depth otherwise not possible without changing the role of the robot as the decision maker

Force-based control for human-robot cooperative object manipulation

In Physical Human-Robot Interaction (PHRI), humans and robots share the workspace and physically interact and collaborate to perform a common task. However, robots do not have human levels of intelligence or the capacity to adapt in performing collaborative tasks. Moreover, the presence of humans in the vicinity of the robot requires ensuring their safety, both in terms of software and hardware. One of the aspects related to safety is the stability of the human-robot control system, which can be placed in jeopardy due to several factors such as internal time delays. Another aspect is the mutual understanding between humans and robots to prevent conflicts in performing a task. The kinesthetic transmission of the human intention is, in general, ambiguous when an object is involved, and the robot cannot distinguish the human intention to rotate from the intention to translate (the translation/rotation problem).This thesis examines the aforementioned issues related to PHRI. First, the instability arising due to a time delay is addressed. For this purpose, the time delay in the system is modeled with the exponential function, and the effect of system parameters on the stability of the interaction is examined analytically. The proposed method is compared with the state-of-the-art criteria used to study the stability of PHRI systems with similar setups and high human stiffness. Second, the unknown human grasp position is estimated by exploiting the interaction forces measured by a force/torque sensor at the robot end effector. To address cases where the human interaction torque is non-zero, the unknown parameter vector is augmented to include the human-applied torque. The proposed method is also compared via experimental studies with the conventional method, which assumes a contact point (i.e., that human torque is equal to zero). Finally, the translation/rotation problem in shared object manipulation is tackled by proposing and developing a new control scheme based on the identification of the ongoing task and the adaptation of the robot\u27s role, i.e., whether it is a passive follower or an active assistant. This scheme allows the human to transport the object independently in all degrees of freedom and also reduces human effort, which is an important factor in PHRI, especially for repetitive tasks. Simulation and experimental results clearly demonstrate that the force required to be applied by the human is significantly reduced once the task is identified

Predicting human motion intention for pHRI assistive control

This work addresses human intention identification during physical Human-Robot Interaction (pHRI) tasks to include this information in an assistive controller. To this purpose, human intention is defined as the desired trajectory that the human wants to follow over a finite rolling prediction horizon so that the robot can assist in pursuing it. This work investigates a Recurrent Neural Network (RNN), specifically, Long-Short Term Memory (LSTM) cascaded with a Fully Connected layer. In particular, we propose an iterative training procedure to adapt the model. Such an iterative procedure is powerful in reducing the prediction error. Still, it has the drawback that it is time-consuming and does not generalize to different users or different co-manipulated objects. To overcome this issue, Transfer Learning (TL) adapts the pre-trained model to new trajectories, users, and co-manipulated objects by freezing the LSTM layer and fine-tuning the last FC layer, which makes the procedure faster. Experiments show that the iterative procedure adapts the model and reduces prediction error. Experiments also show that TL adapts to different users and to the co-manipulation of a large object. Finally, to check the utility of adopting the proposed method, we compare the proposed controller enhanced by the intention prediction with the other two standard controllers of pHRI

Contact force regulation in physical human-machine interaction based on model predictive control

With increasing attention to physical human-machine interaction (pHMI), new control methods involving contact force regulation in collaborative and coexistence scenarios have spread in recent years. Thanks to its internal robustness, high dynamic performance, and capabilities to avoid constraint violations, a Model Predictive Control (MPC) action can pose a viable solution to manage the uncertainties involved in those applications. This paper uses an MPC-driven control method that aims to apply a well-defined and tunable force impulse on a human subject. After describing a general control design suitable to achieve this goal, a practical implementation of such a logic, based on an MPC controller, is shown. In particular, the physical interaction considered is the one occurring between the body of a patient and an external perturbation device in a dynamic posturography trial. The device prototype is presented in both its hardware architecture and software design. The MPC-based main control parameters are thus tuned inside hardware-in-the-loop and human-in-the-loop environments to get optimal behaviors. Finally, the device performance is analyzed to assess the MPC algorithm’s accuracy, repeatability, flexibility, and robustness concerning the several uncertainties due to the specific pHMI environment considered

Collaborative Bimanual Manipulation Using Optimal Motion Adaptation and Interaction Control Retargetting Human Commands to Feasible Robot Control References

This article presents a robust and reliable human–robot collaboration (HRC) framework for bimanual manipulation. We propose an optimal motion adaptation method to retarget arbitrary human commands to feasible robot pose references while maintaining payload stability. The framework comprises three modules: 1) a task-space sequential equilibrium and inverse kinematics optimization ( task-space SEIKO ) for retargeting human commands and enforcing feasibility constraints, 2) an admittance controller to facilitate compliant human–robot physical interactions, and 3) a low-level controller improving stability during physical interactions. Experimental results show that the proposed framework successfully adapted infeasible and dangerous human commands into continuous motions within safe boundaries and achieved stable grasping and maneuvering of large and heavy objects on a real dual-arm robot via teleoperation and physical interaction. Furthermore, the framework demonstrated the capability in the assembly task of building blocks and the insertion task of industrial power connectors

Automatic Electromechanical Perturbator for Postural Control Analysis Based on Model Predictive Control

Objective clinical analyses are required to evaluate balance control performance. To this outcome, it is relevant to study experimental protocols and to develop devices that can provide reliable information about the ability of a subject to maintain balance. Whereas most of the applications available in the literature and on the market involve shifting and tilting of the base of support, the system presented in this paper is based on the direct application of an impulsive (short-lasting) force by means of an electromechanical device (named automatic perturbator). The control of such stimulation is rather complex since it requires high dynamics and accuracy. Moreover, the occurrence of several non-linearities, mainly related to the human–machine interaction, signals the necessity for robust control in order to achieve the essential repeatability and reliability. A linear electric motor, in combination with Model Predictive Control, was used to develop an automatic perturbator prototype. A test bench, supported by model simulations, was developed to test the architecture of the perturbation device. The performance of the control logic has been optimized by iterative tuning of the controller parameters, and the resulting behavior of the automatic perturbator is presented

Control interfaces for active trunk support

People with Duchenne muscular dystrophy (DMD) lose the ability to move due to severe muscular weakness hindering their activities of daily living (ADL). As a consequence, they have difficulties with remaining independent and have to depend on caregivers. Medication cannot prevent or cure DMD but it can increase the life expectancy of patients. Notwithstanding the increase in life expectancy, people with DMD have a lower Health Related Quality of Life (HRQoL) compared to people without DMD. A possible improvement could be achieved with assistive devices to perform ADL and, as a result, to depend less on caregivers.Symbionics (2.1) has been focusing on developing dynamic trunk and head supportive devices for people with neuromuscular disorders to assist them when performing daily activities. Three sub-projects were defined; they investigated user involvement, passive trunk support and active trunk support. User involvement entailed the interaction between the trunk and arm when accomplishing daily tasks. A passive trunk support was designed and tested in an experimental environment by people without and with an early stage of DMD. As the DMD progresses, more assistance is needed which could possibly be provided by an active trunk support. Thus, an active trunk support (which is the focus of this thesis) concentrates on the actuation and control of a passive trunk support.Operating and controlling an active assistive device requires a control interface. The control interface is responsible for converting the intended movement of the user into a device movement. Several control interfaces have been proposed for the control of assistive devices, the most common ones being a joystick, force sensors and sEMG (surface electromyography). We evaluated their performance by building an experimental user-controllable trunk support.The goal of this dissertation, therefore, is to evaluate control interfaces for active trunk support.To this end, several research questions were formulated and investigated:I. Is there an alternative to the intuitive trunk control interface to steer trunk muscles?Current research on the control of orthotic devices that use sEMG signals as control inputs, focuses mainly on muscles that are directly linked to the movement being performed (intuitive control). However, in some cases, it is hard to detect a proper sEMG signal (e.g., when there is a significant amount of fat) or specifically for EMG from trunk muscles, respiratory muscles are located in the trunk as well and can easily disturb the control signal, which can result in poor control performance. A way to overcome this problem might be the introduction of other, non-intuitive forms of control. We performed an explorative, comparative study on the learning behaviour of two different control interfaces, one with trunk muscle sEMGs (intuitive) and one with leg muscle sEMGs (non-intuitive) that can be potentially used for an active trunk support. Six healthy subjects undertook a 2-D Fitts’ law style task. They were asked to steer a cursor towards targets that were radially distributed symmetrically in five directions. II. Which control interface aids an active trunk support better?A feasibility study evaluated control interface performance with a novel trunk support assistive device (Trunk Drive) for adult men with Duchenne Muscular Dystrophy (DMD) namely, joystick, force on sternum, force on feet and sEMG (electromyography). This was done as a discrete position tracking task. We built a one degree of freedom active trunk support device that was tested on 10 healthy men. An experiment, based on Fitts’ law, was conducted for the evaluation. III. Which assistive admittance controller performs best in a 1-D Fitts’ law task?This study was dedicated to the development and assessment of three different admittance control algorithms for a trunk supporting robot; a law with constant parameters, a law with added feedforward force, and a law with variable parameters. A Fitts’-like experiment with 12 healthy subjects was performed to compare the control laws. IV. Do people with DMD generate satisfactory signals which can potentially drive an active trunk support?In a previous study, we showed that healthy people were able to control an active trunk support using four different control interfaces (based on joystick, force on feet, force on sternum and sEMG). All four control interfaces had different advantages and disadvantages. The aim of this study was to explore which of the four inputs could be detectably used by people with DMD to control an active trunk support. Three subjects with DMD participated in two experiments: an active experiment with an active trunk support assistive device and a static experiment without the active trunk support. The challenge in both experiments was to steer the cursor into a target of a graphical user interface using the signals from the different control interfaces. We concluded that, although the non-intuitive force on feet control is one of the best interfaces for people with DMD to control an active trunk support some DMD patients find it easier to use the EMG from their leg muscles. The joystick is the only usable intuitive control interface but, the function of one hand has to be sacrificed. The decision, as to which control interface works best, must be made per individual.<br/