Modeling of physical human–robot interaction : admittance controllers applied to intelligent assist devices with large payload

Enhancement of human performance using an intelligent assist device is becoming more common. In order to achieve effective augmentation of human capacity, cooperation between human and robot must be safe and very intuitive. Ensuring such collaboration remains a challenge, especially when admittance control is used. This paper addresses the issues of transparency and human perception coming from vibration in admittance control schemes. Simulation results obtained with our suggested improved model using an admittance controller are presented, then four models using transfer functions are discussed in detail and evaluated as a means of simulating physical human–robot interaction using admittance control. The simulation and experimental results are then compared in order to assess the validity and limitations of the proposed models in the case of a four-degree-of-freedom intelligent assist device designed for large payload

Safe Human Robot-Interaction using Switched Model Reference Admittance Control

Physical Human-Robot Interaction (pHRI) task involves tight coupling between safety constraints and compliance with human intentions. In this paper, a novel switched model reference admittance controller is developed to maintain compliance with the external force while upholding safety constraints in the workspace for an n-link manipulator involved in pHRI. A switched reference model is designed for the admittance controller to generate the reference trajectory within the safe workspace. The stability analysis of the switched reference model is carried out by an appropriate selection of the Common Quadratic Lyapunov Function (CQLF) so that asymptotic convergence of the trajectory tracking error is ensured. The efficacy of the proposed controller is validated in simulation on a two-link robot manipulator

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

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

Human-Robot Collaboration for Kinesthetic Teaching

Recent industrial interest in producing smaller volumes of products in shorter time frames, in contrast to mass production in previous decades, motivated the introduction of human–robot collaboration (HRC) in industrial settings, as an attempt to increase flexibility in manufacturing applications by incorporating human intelligence and dexterity to these processes. This thesis presents methods for improving the involvement of human operators in industrial settings where robots are present, with a particular focus on kinesthetic teaching, i.e., manually guiding the robot to define or correct its motion, since it can facilitate non-expert robot programming.To increase flexibility in the manufacturing industry implies a loss of a fixed structure of the industrial environment, which increases the uncertainties in the shared workspace between humans and robots. Two methods have been proposed in this thesis to mitigate such uncertainty. First, null-space motion was used to increase the accuracy of kinesthetic teaching by reducing the joint static friction, or stiction, without altering the execution of the robotic task. This was possible since robots used in HRC, i.e., collaborative robots, are often designed with additional degrees of freedom (DOFs) for a greater dexterity. Second, to perform effective corrections of the motion of the robot through kinesthetic teaching in partially-unknown industrial environments, a fast identification of the source of robot–environment contact is necessary. Fast contact detection and classification methods in literature were evaluated, extended, and modified to use them in kinesthetic teaching applications for an assembly task. For this, collaborative robots that are made compliant with respect to their external forces/torques (as an active safety mechanism) were used, and only embedded sensors of the robot were considered.Moreover, safety is a major concern when robotic motion occurs in an inherently uncertain scenario, especially if humans are present. Therefore, an online variation of the compliant behavior of the robot during its manual guidance by a human operator was proposed to avoid undesired parts of the workspace of the robot. The proposed method used safety control barrier functions (SCBFs) that considered the rigid-body dynamics of the robot, and the method’s stability was guaranteed using a passivity-based energy-storage formulation that includes a strict Lyapunov function.All presented methods were tested experimentally on a real collaborative robot

Whole-Body Control of a Mobile Manipulator for Passive Collaborative Transportation

Human-robot collaborative tasks foresee interactions between humans and robots with various degrees of complexity. Specifically, for tasks which involve physical contact among the agents, challenges arise in the modelling and control of such interaction. In this paper we propose a control architecture capable of ensuring a flexible and robustly stable physical human-robot interaction, focusing on a collaborative transportation task. The architecture is deployed onto a mobile manipulator, modelled as a whole-body structure, which aids the operator during the transportation of an unwieldy load. Thanks to passivity techniques, the controller adapts its interaction parameters online while preserving robust stability for the overall system, thus experimentally validating the architecture

Dance Teaching by a Robot: Combining Cognitive and Physical Human-Robot Interaction for Supporting the Skill Learning Process

This letter presents a physical human-robot interaction scenario in which a robot guides and performs the role of a teacher within a defined dance training framework. A combined cognitive and physical feedback of performance is proposed for assisting the skill learning process. Direct contact cooperation has been designed through an adaptive impedance-based controller that adjusts according to the partner's performance in the task. In measuring performance, a scoring system has been designed using the concept of progressive teaching (PT). The system adjusts the difficulty based on the user's number of practices and performance history. Using the proposed method and a baseline constant controller, comparative experiments have shown that the PT presents better performance in the initial stage of skill learning. An analysis of the subjects' perception of comfort, peace of mind, and robot performance have shown a significant difference at the p < .01 level, favoring the PT algorithm.Comment: Presented at IEEE International Conference on Robotics and Automation ICRA-201