Achyuthan Unni Krishnan

Echocardiography and ultrasonography are mainstay imaging modalities for the diagnosis, management, and surveillance of widespread acute and chronic illnesses, including cardiovascular disease. As such, they contribute to improved outcomes. Skilled sonographers are currently required to perform complete diagnostic exams; however, a critical shortage of sonographers in isolated, rural, remote, and global locations contributes to known healthcare disparities. Telerobotic ultrasound systems, which assist sonographers in using a robotic arm to examine patients remotely, offers a potentially profound impact. While this approach represents the intersection of telehealth, robotics, and imaging, important logistics must be addressed to achieve widespread adoption. These include integration of systems and workflow, the need for robust networks, cybersecurity, haptic feedback, cost/billing, and regulatory aspects. The utility, development, technical requirements, and future applications for these technologies are described here.

Teleoperation enables controlling complex robot systems remotely, providing the ability to impart human expertise from a distance. However, these interfaces can be complicated to use as it is difficult to contextualize information about robot motion in the workspace from the limited camera feedback. Thus, it is required to study the best manner in which assistance can be provided to the operator that reduces interface complexity and effort required for teleoperation. Some techniques that provide assistance to the operator while freeform teleoperating include: (1) perception augmentation, like augmented reality visual cues and additional camera angles, increasing the information available to the operator; (2) action augmentation, like assistive autonomy and control augmentation, optimized to reduce the effort required by the operator while teleoperating. In this article, we investigate: (1) which aspects of dexterous telemanipulation require assistance; (2) the impact of perception and action augmentation in improving teleoperation performance; and (3) what factors impact the usage of assistance and how to tailor these interfaces based on the operators’ needs and characteristics. The findings from this user study and resulting post-study surveys will help identify task-based and user-preferred perception and augmentation features for teleoperation assistance.

This work compared human-robot shared and supervisory control of remote robots for dexterous manipulation, and examined how the reliability of robot autonomy affects human operator performance, workload, and preference for robot assistance. Specifically, we implemented two human-robot collaboration (HRC) paradigms for remote manipulation: (1) shared control, where humans controlled gross manipulation and the robot autonomy controlled precise manipulation actions, and (2) supervisory control, where the robot autonomy controlled both gross and precise manipulation actions but relied on humans to detect and correct errors. We conducted two user studies: one to compare the effectiveness of the two HRC paradigms when assistive autonomy is reliable, and the other to examine the impact of error type and frequency on tasks and human operators in the two HRC paradigms when assistive autonomy is unreliable. Our results show that: (1) the interface with a higher level of reliable autonomy yields significantly better performance, lower workload, and higher user preference but lower engagement, and (2) the frequency and type of the error have significant impacts on the task performance and human workload but only partially affects the operator's preference and usage of autonomy.

Teleoperation enables complex robot platforms to perform tasks beyond the scope of the current state-of-the-art robot autonomy by imparting human intelligence and critical thinking to these operations. For seamless control of robot platforms, it is essential to facilitate optimal situational awareness of the workspace for the operator through active telepresence cameras. However, the control of these active telepresence cameras adds an additional degree of complexity to the task of teleoperation. In this paper we present our results from the user study that investigates: (1) how the teleoperator learns or adapts to performing the tasks via active cameras modeled after camera placements on the TRINA humanoid robot; (2) the perception-action coupling operators implement to control active telepresence cameras, and (3) the camera preferences for performing the tasks. These findings from the human motion analysis and post-study survey will help us determine desired design features for robot teleoperation interfaces and assistive autonomy.

Motion tracking interfaces are intuitive for free-form teleoperation tasks. However, efficient manipulation control can be difficult with such interfaces because of issues like the interference of unintended motions and the limited precision of human motion control. The limitation in control efficiency reduces the operator's performance and increases their workload and frustration during robot teleoperation. To improve the efficiency, we proposed separating controlled degrees of freedom (DoFs) and adjusting the motion scaling ratio of a motion tracking interface. The motion tracking of handheld controllers from a Virtual Reality system was used for the interface. We separated the translation and rotational control into: 1) two controllers held in the dominant and non-dominant hands and 2) hand pose tracking and trackpad inputs of a controller. We scaled the control mapping ratio based on 1) the environmental constraints and 2) the teleoperator's control speed. We further conducted a user study to investigate the effectiveness of the proposed methods in increasing efficiency. Our results show that the separation of position and orientation control into two controllers and the environment-based scaling methods perform better than their alternatives.

Robot teleoperation via motion mapping has been demonstrated to be an efficient and intuitive approach for controlling and teaching the whole-body motion coordination of humanoid robots. However, the physical fatigue in the usage of such robot teleoperation interfaces may prevent this approach to be widely used in large scale by diverse workforce populations. As a result, this paper conducts a user study to investigate the physical fatigue of teleoperators in the whole-body motion mapping teleoperation of a mobile humanoid assistive robot. Through a Vicon motion capture system, participants teleoperated the robot to perform general purpose assistive tasks that involve reaching-to-grasp, bimanual manipulation, loco-manipulation and human-robot interaction. We assess the physical fatigue based on surface electromyography (sEMG) measurement, and compare it between different tasks and muscles. Our analysis results indicate that: (1) Fatigue happens more in the tasks that involve more precise manipulation and steady posture maintenance; (2) Deltoids, Biceps and Trapezius are used more for such tasks and thus have more fatigue than others. These findings imply that automating the fatigue-causing task components may reduce the physical fatigue in motion mapping teleoperation.