8 research outputs found
Shear-invariant Sliding Contact Perception with a Soft Tactile Sensor
Manipulation tasks often require robots to be continuously in contact with an
object. Therefore tactile perception systems need to handle continuous contact
data. Shear deformation causes the tactile sensor to output path-dependent
readings in contrast to discrete contact readings. As such, in some
continuous-contact tasks, sliding can be regarded as a disturbance over the
sensor signal. Here we present a shear-invariant perception method based on
principal component analysis (PCA) which outputs the required information about
the environment despite sliding motion. A compliant tactile sensor (the TacTip)
is used to investigate continuous tactile contact. First, we evaluate the
method offline using test data collected whilst the sensor slides over an edge.
Then, the method is used within a contour-following task applied to 6 objects
with varying curvatures; all contours are successfully traced. The method
demonstrates generalisation capabilities and could underlie a more
sophisticated controller for challenging manipulation or exploration tasks in
unstructured environments. A video showing the work described in the paper can
be found at https://youtu.be/wrTM61-pieUComment: Accepted in ICRA 201
Control System and Graphical User Interface Design of an Upper-Extremity Rehabilitation Robot
Stroke is one of the leading causes of death, physical disability, and loss of brain functionality each year, especially amongst older adults. The ability to access good quality post-stroke rehabilitation exercises is essential for stroke survivors to maximize their potential to regain skills and physical abilities. Robot-assisted therapy is showing promise as a way to provide stroke survivors with engaging, challenging, and repetitive tasks while delivering measured therapy that is able to objectively evaluate patients’ progress. Among several challenges that are associated with the design of rehabilitation robots (e.g., the mechanical structure, the actuator types, the control strategies), the design of the control strategy is one of the most critical. Depending on the type of patient and the severity of the impairment of motor control, various control strategies could be applied
for the recovery of the impaired limb in stroke survivors using robot-assisted therapy.
Research is needed into the development of how best to control rehabilitation robots; this
includes both the internal control algorithms and the User Interface (UI) for therapists.
As such, the first objective of this research is to design and implement a motion controller and force-field controller for a 2-Degree of Freedom (DOF) manipulandum upper-extremity rehabilitation robot that is able to deliver planar rehabilitation exercises for stroke survivors while taking therapeutic rehabilitation goals into account. The motion
control algorithm can precisely follow a prescribed time-dependent trajectory whereas the
force-control method will only provide assistance (or even resistance to introduce extra
challenge) to the patient to do the task rather than forcing the movement. For doing the simulation studies, a motor control model of post-stroke patients was proposed. The effectiveness of these controllers was explored in simulations and it was observed that the developed force-field algorithm had a positive effect on the motor control recovery for a
simulated patient. The simulation results also indicated that the resistive mode of therapy would result in better outcomes after the therapy which aligns with experimental studies by other researchers. In addition, a novel adaptive algorithm was proposed for fine-tuning the proposed force-field parameters based on the performance of the patient during the therapy as a subject specific controller can help to achieve a desirable performance for each patient. While this approach is promising, the effectiveness of the adaptation rule has yet to be evaluated on real patients in the future.
To enable effective access and use of the robot, the controller needs to be visualized through a Graphical User Interface (GUI) in a way that therapists can understand and use. The second goal of this thesis research was to work with therapists to collaboratively design an intuitive to use GUI for therapists to control the robot and provide objective information on patients’ performance. The identification of features and feedback on the intuitiveness of the GUI developed in this research highlights the value of collaborative design between engineers and therapists to create the interface that enables therapists to
control the rehabilitation robot. This research also identifies the need for collaborative GUI design with patients as their needs and preference may be different from therapists. During the collaborative GUI design, it was observed that including obstacles and force-field method might be a possible useful method for supporting patients’ movement trajectory, not only because therapists can adjust the force strength to suit a specific patient, but also because they can use its numerical data for objective measurement of patients’ performance. Therapists who participated in this research stated that objective measurements (i.e., trajectory smoothness, speed, mobility range, and error) could be used to evaluate the patient performance. While rehabilitation robots are different in terms of mechanical structure, work-space, and the exercise that they can provide, similar methods could be used for supporting patients’ movement trajectory and performance evaluation. As the GUI is the first prototype, it needs to be used with and evaluated by therapists and patients to ascertain if the information presented in the GUI is intuitive and to explore if they can understand it or use it
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A Unified Visual-Haptic Fingertip Sensor For Advanced Robot Dexterity
The problem of robotic grasping and manipulation requires a system level perspective that needs to be aimed at solving the interlinked sub-problems simultaneously. These sub-problems consists of designing an appropriate robot hand, sensing technology, control, and planning strategy, that can increase the dexterity of a robot hand in complex environments. Approaches towards these lack the proper use and integration of tactile feedback that can potentially enable robot hands with far superior capabilities than found today. This thesis addresses this challenge from three aspects: hardware design, system integration, and algorithm development. On the hardware side, it traces the thorough development of a multi and cross-modal tactile sensor that can measure proximity, contact, and force (PCF). Three unique features of the PCF sensor are (i) the ability to measure visual as well as tactile object features, (ii) its low manufacturing cost and (iii) that it can be easily integrated into different type of robot hands. This is achieved by embedding infrared proximity sensing integrated chips in soft elastomer to achieve a multitude of signals. On the system integration side, the thesis manifests the individual importance of the hand design, visual and tactile sensing modalities in the context of robotic manipulation related tasks through careful real-world robotic experiments. On the algorithmic side, it shows the implementation of several algorithms concerning signal processing, computer vision, controls, probabilistic theory and machine learning for experimental evaluation.</p