Position referenced force augmentation in teleoperated hydraulic manipulators operating under delayed and lossy networks: a pilot study.

Position error between motions of the master and slave end-effectors is inevitable as it originates from hard-to-avoid imperfections in controller design and model uncertainty. Moreover, when a slave manipulator is controlled through a delayed and lossy communication channel, the error between the desired motion originating from the master device and the actual movement of the slave manipulator end-effector is further exacerbated. This paper introduces a force feedback scheme to alleviate this problem by simply guiding the operator to slow down the haptic device motion and, in turn, allows the slave manipulator to follow the desired trajectory closely. Using this scheme, the master haptic device generates a force, which is proportional to the position error at the slave end-effector, and opposite to the operator's intended motion at the master site. Indeed, this force is a signal or cue to the operator for reducing the hand speed when position error, due to delayed and lossy network, appears at the slave site. Effectiveness of the proposed scheme is validated by performing experiments on a hydraulic telemanipulator setup developed for performing live-line maintenance. Experiments are conducted when the system operates under both dedicated and wireless networks. Results show that the scheme performs well in reducing the position error between the haptic device and the slave end-effector. Specifically, by utilizing the proposed force, the mean position error, for the case presented here, reduces by at least 92% as compared to the condition without the proposed force augmentation scheme. The scheme is easy to implement, as the only required on-line measurement is the angular displacement of the slave manipulator joints

Model-Augmented Haptic Telemanipulation: Concept, Retrospective Overview, and Current Use Cases

Certain telerobotic applications, including telerobotics in space, pose particularly demanding challenges to both technology and humans. Traditional bilateral telemanipulation approaches often cannot be used in such applications due to technical and physical limitations such as long and varying delays, packet loss, and limited bandwidth, as well as high reliability, precision, and task duration requirements. In order to close this gap, we research model-augmented haptic telemanipulation (MATM) that uses two kinds of models: a remote model that enables shared autonomous functionality of the teleoperated robot, and a local model that aims to generate assistive augmented haptic feedback for the human operator. Several technological methods that form the backbone of the MATM approach have already been successfully demonstrated in accomplished telerobotic space missions. On this basis, we have applied our approach in more recent research to applications in the fields of orbital robotics, telesurgery, caregiving, and telenavigation. In the course of this work, we have advanced specific aspects of the approach that were of particular importance for each respective application, especially shared autonomy, and haptic augmentation. This overview paper discusses the MATM approach in detail, presents the latest research results of the various technologies encompassed within this approach, provides a retrospective of DLR's telerobotic space missions, demonstrates the broad application potential of MATM based on the aforementioned use cases, and outlines lessons learned and open challenges

Trajectory online adaption based on human motion prediction for teleoperation

In this work, a human motion intention prediction method based on an autoregressive (AR) model for teleoperation is developed. Based on this method, the robot's motion trajectory can be updated in real time through updating the parameters of the AR model. In the teleoperated robot's control loop, a virtual force model is defined to describe the interaction profile and to correct the robot's motion trajectory in real time. The proposed human motion prediction algorithm acts as a feedforward model to update the robot's motion and to revise this motion in the process of human-robot interaction (HRI). The convergence of this method is analyzed theoretically. Comparative studies demonstrate the enhanced performance of the proposed approach

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

Augmented Reality and Robotics: A Survey and Taxonomy for AR-enhanced Human-Robot Interaction and Robotic Interfaces

This paper contributes to a taxonomy of augmented reality and robotics based on a survey of 460 research papers. Augmented and mixed reality (AR/MR) have emerged as a new way to enhance human-robot interaction (HRI) and robotic interfaces (e.g., actuated and shape-changing interfaces). Recently, an increasing number of studies in HCI, HRI, and robotics have demonstrated how AR enables better interactions between people and robots. However, often research remains focused on individual explorations and key design strategies, and research questions are rarely analyzed systematically. In this paper, we synthesize and categorize this research field in the following dimensions: 1) approaches to augmenting reality; 2) characteristics of robots; 3) purposes and benefits; 4) classification of presented information; 5) design components and strategies for visual augmentation; 6) interaction techniques and modalities; 7) application domains; and 8) evaluation strategies. We formulate key challenges and opportunities to guide and inform future research in AR and robotics

Flexible assimilation of human's target for versatile human-robot physical interaction

Recent studies on the physical interaction between humans have revealed their ability to read the partner's motion plan and use it to improve one's own control. Inspired by these results, we develop an intention assimilation controller (IAC) that enables a contact robot to estimate the human's virtual target from the interaction force, and combine it with its own target to plan motion. While the virtual target depends on the control gains assumed for the human, we show that this does not affect the stability of the human-robot system, and our novel scheme covers a continuum of interaction behaviours from assistance to competition. Simulations and experiments illustrate how the IAC can assist the human or compete with them to prevent collisions. We demonstrate the IAC's advantages over related methods, such as faster convergence to a target, guidance with less force, safer obstacle avoidance and a wider range of interaction behaviours

A Stable and Transparent Framework for Adaptive Shared Control of Robots

In mixed-initiative haptic shared control of robots, humans and automatic control system work in parallel. The commands to the robot are a weighted sum of forces from these two agents. This thesis develops control methods to improve the force feedback performance for mixed-initiative shared teleoperation and to adapt the control authority between human and automatic control system in a stable manner even in the presence of communication delays. All methods are validated on real robotic hardware

A Haptic Shared-Control Architecture for Guided Multi-Target Robotic Grasping

Although robotic telemanipulation has always been a key technology for the nuclear industry, little advancement has been seen over the last decades. Despite complex remote handling requirements, simple mechanically linked master-slave manipulators still dominate the field. Nonetheless, there is a pressing need for more effective robotic solutions able to significantly speed up the decommissioning of legacy radioactive waste. This paper describes a novel haptic shared-control approach for assisting a human operator in the sort and segregation of different objects in a cluttered and unknown environment. A three-dimensional scan of the scene is used to generate a set of potential grasp candidates on the objects at hand. These grasp candidates are then used to generate guiding haptic cues, which assist the operator in approaching and grasping the objects. The haptic feedback is designed to be smooth and continuous as the user switches from a grasp candidate to the next one, or from one object to another one, avoiding any discontinuity or abrupt changes. To validate our approach, we carried out two human-subject studies, enrolling 15 participants. We registered an average improvement of 20.8%, 20.1%, and 32.5% in terms of completion time, linear trajectory, and perceived effectiveness, respectively, between the proposed approach and standard teleoperation