Increasing Transparency and Presence of Teleoperation Systems Through Human-Centered Design

Teleoperation allows a human to control a robot to perform dexterous tasks in remote, dangerous, or unreachable environments. A perfect teleoperation system would enable the operator to complete such tasks at least as easily as if he or she was to complete them by hand. This ideal teleoperator must be perceptually transparent, meaning that the interface appears to be nearly nonexistent to the operator, allowing him or her to focus solely on the task environment, rather than on the teleoperation system itself. Furthermore, the ideal teleoperation system must give the operator a high sense of presence, meaning that the operator feels as though he or she is physically immersed in the remote task environment. This dissertation seeks to improve the transparency and presence of robot-arm-based teleoperation systems through a human-centered design approach, specifically by leveraging scientific knowledge about the human motor and sensory systems. First, this dissertation aims to improve the forward (efferent) teleoperation control channel, which carries information from the human operator to the robot. The traditional method of calculating the desired position of the robot\u27s hand simply scales the measured position of the human\u27s hand. This commonly used motion mapping erroneously assumes that the human\u27s produced motion identically matches his or her intended movement. Given that humans make systematic directional errors when moving the hand under conditions similar to those imposed by teleoperation, I propose a new paradigm of data-driven human-robot motion mappings for teleoperation. The mappings are determined by having the human operator mimic the target robot as it autonomously moves its arm through a variety of trajectories in the horizontal plane. Three data-driven motion mapping models are described and evaluated for their ability to correct for the systematic motion errors made in the mimicking task. Individually-fit and population-fit versions of the most promising motion mapping model are then tested in a teleoperation system that allows the operator to control a virtual robot. Results of a user study involving nine subjects indicate that the newly developed motion mapping model significantly increases the transparency of the teleoperation system. Second, this dissertation seeks to improve the feedback (afferent) teleoperation control channel, which carries information from the robot to the human operator. We aim to improve a teleoperation system a teleoperation system by providing the operator with multiple novel modalities of haptic (touch-based) feedback. We describe the design and control of a wearable haptic device that provides kinesthetic grip-force feedback through a geared DC motor and tactile fingertip-contact-and-pressure and high-frequency acceleration feedback through a pair of voice-coil actuators mounted at the tips of the thumb and index finger. Each included haptic feedback modality is known to be fundamental to direct task completion and can be implemented without great cost or complexity. A user study involving thirty subjects investigated how these three modalities of haptic feedback affect an operator\u27s ability to control a real remote robot in a teleoperated pick-and-place task. This study\u27s results strongly support the utility of grip-force and high-frequency acceleration feedback in teleoperation systems and show more mixed effects of fingertip-contact-and-pressure feedback

Teleoperating a mobile manipulator and a free-flying camera from a single haptic device

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThe paper presents a novel teleoperation system that allows the simultaneous and continuous command of a ground mobile manipulator and a free flying camera, implemented using an UAV, from which the operator can monitor the task execution in real-time. The proposed decoupled position and orientation workspace mapping allows the teleoperation from a single haptic device with bounded workspace of a complex robot with unbounded workspace. When the operator is reaching the position and orientation boundaries of the haptic workspace, linear and angular velocity components are respectively added to the inputs of the mobile manipulator and the flying camera. A user study on a virtual environment has been conducted to evaluate the performance and the workload on the user before and after proper training. Analysis on the data shows that the system complexity is not an obstacle for an efficient performance. This is a first step towards the implementation of a teleoperation system with a real mobile manipulator and a low-cost quadrotor as the free-flying camera.Accepted versio

Planning and Real Time Control of a Minimally Invasive Robotic Surgery System

This paper introduces the planning and control software of a teleoperating robotic system for minimally invasive surgery. It addresses the problem of how to organize a complex system with 41 degrees of freedom including robot setup planning, force feedback control and nullspace handling with three robotic arms. The planning software is separated into sequentially executed planning and registration procedures. An optimal setup is first planned in virtual reality and then adapted to variations in the operating room. The real time control system is composed of hierarchical layers. The design is flexible and expandable without losing performance. Structure, functionality and implementation of planning and control are described. The robotic system provides the surgeon with an intuitive hand-eye-coordination and force feedback in teleoperation for both hands

Autonomy Infused Teleoperation with Application to BCI Manipulation

Robot teleoperation systems face a common set of challenges including latency, low-dimensional user commands, and asymmetric control inputs. User control with Brain-Computer Interfaces (BCIs) exacerbates these problems through especially noisy and erratic low-dimensional motion commands due to the difficulty in decoding neural activity. We introduce a general framework to address these challenges through a combination of computer vision, user intent inference, and arbitration between the human input and autonomous control schemes. Adjustable levels of assistance allow the system to balance the operator's capabilities and feelings of comfort and control while compensating for a task's difficulty. We present experimental results demonstrating significant performance improvement using the shared-control assistance framework on adapted rehabilitation benchmarks with two subjects implanted with intracortical brain-computer interfaces controlling a seven degree-of-freedom robotic manipulator as a prosthetic. Our results further indicate that shared assistance mitigates perceived user difficulty and even enables successful performance on previously infeasible tasks. We showcase the extensibility of our architecture with applications to quality-of-life tasks such as opening a door, pouring liquids from containers, and manipulation with novel objects in densely cluttered environments

Assistance strategies for robotized laparoscopy

Robotizing laparoscopic surgery not only allows achieving better accuracy to operate when a scale factor is applied between master and slave or thanks to the use of tools with 3 DoF, which cannot be used in conventional manual surgery, but also due to additional informatic support. Relying on computer assistance different strategies that facilitate the task of the surgeon can be incorporated, either in the form of autonomous navigation or cooperative guidance, providing sensory or visual feedback, or introducing certain limitations of movements. This paper describes different ways of assistance aimed at improving the work capacity of the surgeon and achieving more safety for the patient, and the results obtained with the prototype developed at UPC.Peer ReviewedPostprint (author's final draft

Towards retrieving force feedback in robotic-assisted surgery: a supervised neuro-recurrent-vision approach

Robotic-assisted minimally invasive surgeries have gained a lot of popularity over conventional procedures as they offer many benefits to both surgeons and patients. Nonetheless, they still suffer from some limitations that affect their outcome. One of them is the lack of force feedback which restricts the surgeon's sense of touch and might reduce precision during a procedure. To overcome this limitation, we propose a novel force estimation approach that combines a vision based solution with supervised learning to estimate the applied force and provide the surgeon with a suitable representation of it. The proposed solution starts with extracting the geometry of motion of the heart's surface by minimizing an energy functional to recover its 3D deformable structure. A deep network, based on a LSTM-RNN architecture, is then used to learn the relationship between the extracted visual-geometric information and the applied force, and to find accurate mapping between the two. Our proposed force estimation solution avoids the drawbacks usually associated with force sensing devices, such as biocompatibility and integration issues. We evaluate our approach on phantom and realistic tissues in which we report an average root-mean square error of 0.02 N.Peer ReviewedPostprint (author's final draft