Volkan Patoglu

Design, Control and Performance of RiceWrist: A Force Feedback Wrist Exoskeleton for Rehabilitation and Training

This paper presents the design, control and performance of a high fidelity four degree-of-freedom wrist exoskeleton robot, RiceWrist, for training and rehabilitation. The RiceWrist is intended to provide kinesthetic feedback during the training of motor skills or rehabilitation of reaching movements. Motivation for such applications is based on findings that show robot-assisted physical therapy aids in the rehabilitation process following neurological injuries. The exoskeleton device accommodates forearm supination and pronation, wrist flexion and extension and radial and ulnar deviation in a compact parallel mechanism design with low friction, zero backlash and high stiffness. As compared to other exoskeleton devices, the RiceWrist allows easy measurement of human joint angles and independent kinesthetic feedback to individual human joints. In this paper, joint-space as well as task-space position controllers and an impedance-based force controller for the device are presented. The kinematic performance of the device is characterized in terms of its workspace, singularities, manipulability, backlash and backdrivability. The dynamic performance of RiceWrist is characterized in terms of motor torque output, joint friction, step responses, behavior under closed loop set-point and trajectory tracking control and display of virtual walls. The device is singularity-free, encompasses most of the natural workspace of the human joints and exhibits low friction, zero-backlash and high manipulability, which are kinematic properties that characterize a high-quality impedance display device. In addition, the device displays fast, accurate response under position control that matches human actuation bandwidth and the capability to display sufficiently hard contact with little coupling between controlled degrees-of-freedom.

Combining high-level causal reasoning with low-level geometric reasoning and motion planning for robotic manipulation

We present a formal framework that combines high-level representation and causality-based reasoning with low-level geometric reasoning and motion planning. The frame-work features bilateral interaction between task and motion planning, and embeds geometric reasoning in causal reasoning, thanks to several advantages inherited from its underlying components. In particular, our choice of using a causality-based high-level formalism for describing action domains allows us to represent ramifications and state/transition constraints, and embed in such formal domain descriptions externally defined functions implemented in some programming language (e.g., C++). Moreover, given such a domain description, the causal reasoner based on this formalism (i.e., the Causal Calculator) allows us to compute optimal solutions (e.g., shortest plans) for elaborate planning/prediction problems with temporal constraints. Utilizing these features of high-level representation and reasoning, we can combine causal reasoning, motion planning and geometric planning to find feasible kinematic solutions to task-level problems. In our framework, the causal reasoner guides the motion planner by finding an optimal task-plan; if there is no feasible kinematic solution for that task-plan then the motion planner guides the causal reasoner by modifying the planning problem with new temporal constraints. Furthermore, while computing a task-plan, the causal reasoner takes into account geometric models and kinematic relations by means of external predicates implemented for geometric reasoning (e.g., to check some collisions); in that sense the geometric reasoner guides the causal reasoner to find feasible kinematic solutions. We illustrate an application of this framework to robotic manipulation, with two pantograph robots on a complex assembly task that requires concurrent execution of actions. A short video of this application accompanies the paper.

Answer set programming for collaborative housekeeping robotics: representation, reasoning, and execution

Answer set programming (ASP) is a knowledge representation and reasoning paradigm with high-level expressive logic-based formalism, and efficient solvers; it is applied to solve hard problems in various domains, such as systems biology, wire routing, and space shuttle control. In this paper, we present an application of ASP to housekeeping robotics. We show how the following problems are addressed using computational methods/tools of ASP: (1) embedding commonsense knowledge automatically extracted from the commonsense knowledge base ConceptNet, into high-level representation, and (2) embedding (continuous) geometric reasoning and temporal reasoning about durations of actions, into (discrete) high-level reasoning. We introduce a planning and monitoring algorithm for safe execution of plans, so that robots can recover from plan failures due to collision with movable objects whose presence and location are not known in advance or due to heavy objects that cannot be lifted alone. Some of the recoveries require collaboration of robots. We illustrate the applicability of ASP on several housekeeping robotics problems, and report on the computational efficiency in terms of CPU time and memory.

Progressive shared control for training in virtual environments

Virtual environments (VEs) with haptic feedback not only provide a safe and versatile practice medium for many manual control tasks, but also promise to improve the efficacy of manual skill training with the addition of haptic guidance. However, haptic guidance schemes such as shared control may be detrimental since such schemes actively interfere with the coupled system dynamics, thereby causing participants to experience task dynamics that are altered from those of the real task. Therefore, this paper proposes a progressive approach to guidance for training in virtual environments. This progressive guidance scheme adjusts its control gains based on participant performance, aiming to expose the performer to an appropriate amount of haptic guidance throughout training. Long term training experiments were conducted for an under-actuated target-hitting manual control task. The experimental results compare the efficacy of the novel progressive haptic guidance to two common fixed-gain haptic guidance schemes and virtual practice. The results from a month-long training experiment indicate that the proposed progressive shared control scheme reduces guidance interference as compared to fixed-gain guidance schemes thus increasing training efficacy.

Negative efficacy of fixed gain error reducing shared control for training in virtual environments

Virtual reality with haptic feedback provides a safe and versatile practice medium for many manual control tasks. Haptic guidance has been shown to improve performance of manual control tasks in virtual environments; however, the efficacy of haptic guidance for training in virtual environments has not been studied conclusively. This article presents experimental results that show negative efficacy of haptic guidance during training in virtual environments. The haptic guidance in this study is a fixed-gain error-reducing shared controller, with the control effort overlaid on the dynamics of the manual control task during training. Performance of the target-hitting manual control task in the absence of guidance is compared for three training protocols. One protocol contained no haptic guidance and represented virtual practice. Two protocols utilized haptic guidance, varying the duration of exposure to guidance during the training sessions. Exposure to the fixed-gain error-reducing shared controller had a detrimental effect on performance of the target-hitting task at the conclusion of a month-long training protocol, regardless of duration of exposure. While the shared controller was designed with knowledge of the task and an intuitive sense of the motions required to achieve good performance, the results indicate that the acquisition of motor skill is a complex phenomenon that is not aided with haptic guidance during training as implemented in this experiment.

Bridging the Gap between High-Level Reasoning and Low-Level Control

We present a formal framework where a nonmonotonic formalism (the action description language

ASSISTON-SE: A self-aligning shoulder-elbow exoskeleton

{\cal C}+

Design of a reconfigurable ankle rehabilitation robot and its use for the estimation of the ankle impedance

) is used to provide robots with high-level reasoning, such as planning, in the style of cognitive robotics. In particular, we introduce a novel method that bridges the high-level discrete action planning and the low-level continuous behavior by trajectory planning. We show the applicability of this framework on two LEGO MINDSTORMS NXT robots, in an action domain that involves concurrent execution of actions that cannot be serialized.

The RiceWrist: A Distal Upper Extremity Rehabilitation Robot for Stroke Therapy

We present AssistOn-SE:, a novel powered exoskeleton for robot-assisted rehabilitation that allows for movements of the shoulder girdle as well as shoulder rotations. Automatically adjusting its joint axes, AssistOn-SE: can enable a perfect match between human joint axes and the device axes, not only guaranteeing ergonomy and comfort throughout the therapy, but also extending the usable range of motion for the shoulder joint. Moreover, the adjustability feature significantly shortens the setup time required to attach the patient to the exoskeleton, allowing more effective time be spend on exercises instead of wasting this valuable resource for adjustments. Back-driveable design of AssistOn-SE: supports both passive translational movements of the center of glenohumeral joint and independent active control of these degrees of freedom. Thanks to this property, glenohumeral mobilization and scapular stabilization exercises can also be delivered with AssistOn-SE:, extending the type of therapies that can be administered using upper-arm exoskeletons. We introduce the design of the exoskeleton and present the kinematic analysis of its self-aligning joint. We also provide implementation details for an early prototype as well as some experimental results detailing range of motion of the device and its ability to track movements of the shoulder girdle.

Geometric rearrangement of multiple movable objects on cluttered surfaces: A hybrid reasoning approach

This paper presents the design, analysis, and a clinical application of a reconfigurable, parallel mechanism based, force feedback exoskeleton for the human ankle. The device can either be employed as a balance/proprioception trainer or configured to accommodate range of motion (RoM)/strengthening exercises. The exoskeleton can be utilized as a clinical measurement tool to estimate dynamic parameters of the ankle and to assess ankle joint properties in physiological and pathological conditions. Kinematic analysis and control of the device are detailed and a protocol for utilization of the exoskeleton to determine ankle impedance is discussed. The prototype of the device is also presented.