A smart surgical robotic for cochlear implantation

In this paper a surgical robotic device for cochlear implantation surgery is described that is able to discriminate tissue interfaces and other controlling parameters ahead of a drill tip. The advantage in surgery is that tissues at interfaces can be preserved. The smart tool is able to control interaction with respect to the flexing tissue to avoid penetration control the extent of protrusion with respect to the real-time position of the tissue. To interpret drilling conditions, and conditions leading up to breakthrough at a tissue interface, the sensing scheme used enables discrimination between the variety of conditions posed in the drilling environment. The result is a robust fully autonomous system able to respond to tissue type, behaviour and deflection in real-time. The paper describes the robotic tool that has been designed to be used in the surgical environment where it has been used in the operating room.

Robotic Outcome Assessment in a Non-Human Primate Model of Middle Cerebral Artery Stroke

Stroke is a prevalent disorder with immense socioeconomic impact. A variety of chronic neurological deficits result from stroke. In particular, sensorimotor deficits are a significant barrier to achieving post-stroke independence. Unfortunately, the majority of pre-clinical studies that show improved outcomes in animal stroke models have failed in clinical trials. Pre-clinical studies using non-human primate (NHP) stroke models prior to initiating human trials are a potential step to improving translation from animal studies to clinical trials. Robotic assessment tools represent a quantitative, reliable, and reproducible means to assess reaching behaviour following stroke in both humans and NHPs. We investigated the use of robotic technology to assess sensorimotor impairments in NHPs following middle cerebral artery occlusion (MCAO). Two cynomolgus macaques underwent transient MCAO for 90 minutes. Approximately 1.5 years following the procedure these NHPs and two non-stroke control monkeys were trained in a reaching task with both arms in the KINARM exoskeleton. This robot permits elbow and shoulder movements in the horizontal plane. The task required NHPs to make reaching movements from a centrally positioned start target to 1 of 8 peripheral targets uniformly distributed around the first target. We analyzed four movement parameters: reaction time, movement time (MT), initial direction error (IDE), and number of speed maxima to characterize sensorimotor deficiencies. We hypothesized reduced performance in these attributes during a neurobehavioural task with the paretic limb of NHPs following MCAO compared to controls. Reaching movements in the non-affected limbs of control and experimental NHPs showed bell-shaped velocity profiles. In contrast, the reaching movements with the affected limbs were highly variable. We found distinctive patterns in MT, IDE, and number of speed peaks between control and experimental monkeys and between limbs of NHPs with MCAO. NHPs with MCAO demonstrated more speed peaks, longer MTs, and greater IDE in their paretic limb compared to controls. These initial results qualitatively match human stroke subjects’ performance, suggesting that robotic neurobehavioural assessment in NHPs with stroke is feasible and could have translational relevance in subsequent human studies. Further studies will be necessary to replicate and expand on these preliminary findings.

Admittance Control for Robotic Loading: Design and Experiments with a 1-Tonne Loader and a 14-Tonne LHD

This paper describes the design, tuning, and extensive field testing of an admittance-based Autonomous Loading Controller (ALC) for robotic excavation. Several iterations of the ALC were tuned and tested in fragmented rock piles—similar to those found in operating mines—by using both a robotic 1-tonne capacity Kubota R520S diesel-hydraulic surface loader and a 14-tonne capacity Atlas Copco ST14 underground load-haul-dump (LHD) machine. On the R520S loader, the ALC increased payload by 18 % with greater consistency, although with more energy expended and longer dig times when compared with digging at maximum actuator velocity. On the ST14 LHD, the ALC took 61 % less time to load 39 % more payload when compared to a single manual operator. The manual operator made 28 dig attempts by using three different digging strategies, and had one failed dig. The tuned ALC made 26 dig attempts at 10 and 11 MN target force levels. All 10 11 MN digs succeeded while 6 of the 16 10 MN digs failed. The results presented in this paper suggest that the admittance-based ALC is more productive and consistent than manual operators, but that care should be taken when detecting entry into the muck pile

The role of the pharmacist and extemporaneous dispensing: The views of school children.

In: International Journal of Pharmacy Practice 18, Supplement 2, 19-20.

The perihelion activity of comet 67P/Churyumov-Gerasimenko as seen by robotic telescopes

Around the time of its perihelion passage the observability of 67P/Churyumov-Gerasimenko from Earth was limited to very short windows each morning from any given site, due to the low solar elongation of the comet. The peak in the comet's activity was therefore difficult to observe with conventionally scheduled telescopes, but was possible where service/queue scheduled mode was possible, and with robotic telescopes. We describe the robotic observations that allowed us to measure the total activity of the comet around perihelion, via photometry (dust) and spectroscopy (gas), and compare these results with the measurements at this time by Rosetta's instruments. The peak of activity occurred approximately two weeks after perihelion. The total brightness (dust) largely followed the predictions from Snodgrass et al. 2013, with no significant change in total activity levels from previous apparitions. The CN gas production rate matched previous orbits near perihelion, but appeared to be relatively low later in the year.

Real-time tracking system using C80 DSPs and a binocular robotic head

[EN]An active vision system to perform tracking of moving objects in real time is described. The main goal is to obtain a system integrating off-the-self components. These components includes a stereoscopic robotic-head, as active perception hardware; a DSP based board SDB C80, as massive data processor and image acquisition board; and finally, a Pentium PC running Windows NT that interconnects and manages the whole system. Real-time is achieved taking advantage of the special architecture of DSP. An evaluation of the performance is included.

Improving the Accuracy of 3-D Reconstruction in Robotic Vision Applications

Thesis (Ph.D.)--University of Washington, 2016-08

Effect of haptic guidance and error amplification robotic training interventions on the immediate improvement of timing among individuals that had a stroke

Abstract : Many individuals that had a stroke have motor impairments such as timing deficits that hinder their ability to complete daily activities like getting dressed. Robotic rehabilitation is an increasingly popular therapeutic avenue in order to improve motor recovery among this population. Yet, most studies have focused on improving the spatial aspect of movement (e.g. reaching), and not the temporal one (e.g. timing). Hence, the main aim of this study was to compare two types of robotic rehabilitation on the immediate improvement of timing accuracy: haptic guidance (HG), which consists of guiding the person to make the correct movement, and thus decreasing his or her movement errors, and error amplification (EA), which consists of increasing the person’s movement errors. The secondary objective consisted of exploring whether the side of the stroke lesion had an effect on timing accuracy following HG and EA training. Thirty-four persons that had a stroke (average age 67 ± 7 years) participated in a single training session of a timing-based task (simulated pinball-like task), where they had to activate a robot at the correct moment to successfully hit targets that were presented a random on a computer screen. Participants were randomly divided into two groups, receiving either HG or EA. During the same session, a baseline phase and a retention phase were given before and after each training, and these phases were compared in order to evaluate and compare the immediate impact of HG and EA on movement timing accuracy. The results showed that HG helped improve the immediate timing accuracy (p=0.03), but not EA (p=0.45). After comparing both trainings, HG was revealed to be superior to EA at improving timing (p=0.04). Furthermore, a significant correlation was found between the side of stroke lesion and the change in timing accuracy following EA (r[subscript pb]=0.7, p=0.001), but not HG (r[subscript pb]=0.18, p=0.24). In other words, a deterioration in timing accuracy was found for participants with a lesion in the left hemisphere that had trained with EA. On the other hand, for the participants having a right-sided stroke lesion, an improvement in timing accuracy was noted following EA. In sum, it seems that HG helps improve the immediate timing accuracy for individuals that had a stroke. Still, the side of the stroke lesion seems to play a part in the participants’ response to training. This remains to be further explored, in addition to the impact of providing more training sessions in order to assess any long-term benefits of HG or EA.

The Application of a Dendric Cell Algorithm to a Robotic Classifier

The dendritic cell algorithm is an immune-inspired technique for processing time-dependant data. Here we propose it as a possible solution for a robotic classification problem. The dendritic cell algorithm is implemented on a real robot and an investigation is performed into the effects of varying the migration threshold median for the cell population. The algorithm performs well on a classification task with very little tuning. Ways of extending the implementation to allow it to be used as a classifier within the field of robotic security are suggested.

The Application of a Dendric Cell Algorithm to a Robotic Classifier

The dendritic cell algorithm is an immune-inspired technique for processing time-dependant data. Here we propose it as a possible solution for a robotic classification problem. The dendritic cell algorithm is implemented on a real robot and an investigation is performed into the effects of varying the migration threshold median for the cell population. The algorithm performs well on a classification task with very little tuning. Ways of extending the implementation to allow it to be used as a classifier within the field of robotic security are suggested.

The Application of a Dendric Cell Algorithm to a Robotic Classifier

The dendritic cell algorithm is an immune-inspired technique for processing time-dependant data. Here we propose it as a possible solution for a robotic classification problem. The dendritic cell algorithm is implemented on a real robot and an investigation is performed into the effects of varying the migration threshold median for the cell population. The algorithm performs well on a classification task with very little tuning. Ways of extending the implementation to allow it to be used as a classifier within the field of robotic security are suggested.

SUR Hand: A Soft Underactuated Robotic Hand

Soft robots are robots made mostly or completely of soft, deformable, or compliant materials. As humanoid robotic technology takes on a wider range of applications, it has become apparent that they could replace humans in dangerous environments. Current attempts to create robotic hands for these environments are very difficult and costly to manufacture. Therefore, a robotic hand made with simplistic architecture and cheap fabrication techniques is needed. The goal of this thesis is to detail the design, fabrication, modeling, and testing of the SUR Hand. The SUR Hand is a soft, underactuated robotic hand designed to be cheaper and easier to manufacture than conventional hands. Yet, it maintains much of their dexterity and precision. This thesis will detail the design process for the soft pneumatic fingers, compliant palm, and flexible wrist. It will also discuss a semi-empirical model for finger design and the creation and validation of grasping models.

Integrated visual perception architecture for robotic clothes perception and manipulation

This thesis proposes a generic visual perception architecture for robotic clothes perception and manipulation. This proposed architecture is fully integrated with a stereo vision system and a dual-arm robot and is able to perform a number of autonomous laundering tasks. Clothes perception and manipulation is a novel research topic in robotics and has experienced rapid development in recent years. Compared to the task of perceiving and manipulating rigid objects, clothes perception and manipulation poses a greater challenge. This can be attributed to two reasons: firstly, deformable clothing requires precise (high-acuity) visual perception and dexterous manipulation; secondly, as clothing approximates a non-rigid 2-manifold in 3-space, that can adopt a quasi-infinite configuration space, the potential variability in the appearance of clothing items makes them difficult to understand, identify uniquely, and interact with by machine. From an applications perspective, and as part of EU CloPeMa project, the integrated visual perception architecture refines a pre-existing clothing manipulation pipeline by completing pre-wash clothes (category) sorting (using single-shot or interactive perception for garment categorisation and manipulation) and post-wash dual-arm flattening. To the best of the author’s knowledge, as investigated in this thesis, the autonomous clothing perception and manipulation solutions presented here were first proposed and reported by the author. All of the reported robot demonstrations in this work follow a perception-manipulation method- ology where visual and tactile feedback (in the form of surface wrinkledness captured by the high accuracy depth sensor i.e. CloPeMa stereo head or the predictive confidence modelled by Gaussian Processing) serve as the halting criteria in the flattening and sorting tasks, respectively. From scientific perspective, the proposed visual perception architecture addresses the above challenges by parsing and grouping 3D clothing configurations hierarchically from low-level curvatures, through mid-level surface shape representations (providing topological descriptions and 3D texture representations), to high-level semantic structures and statistical descriptions. A range of visual features such as Shape Index, Surface Topologies Analysis and Local Binary Patterns have been adapted within this work to parse clothing surfaces and textures and several novel features have been devised, including B-Spline Patches with Locality-Constrained Linear coding, and Topology Spatial Distance to describe and quantify generic landmarks (wrinkles and folds). The essence of this proposed architecture comprises 3D generic surface parsing and interpretation, which is critical to underpinning a number of laundering tasks and has the potential to be extended to other rigid and non-rigid object perception and manipulation tasks. The experimental results presented in this thesis demonstrate that: firstly, the proposed grasp- ing approach achieves on-average 84.7% accuracy; secondly, the proposed flattening approach is able to flatten towels, t-shirts and pants (shorts) within 9 iterations on-average; thirdly, the proposed clothes recognition pipeline can recognise clothes categories from highly wrinkled configurations and advances the state-of-the-art by 36% in terms of classification accuracy, achieving an 83.2% true-positive classification rate when discriminating between five categories of clothes; finally the Gaussian Process based interactive perception approach exhibits a substantial improvement over single-shot perception. Accordingly, this thesis has advanced the state-of-the-art of robot clothes perception and manipulation.

Upper Limb Posture Estimation in Robotic and Virtual Reality-based Rehabilitation.

New motor rehabilitation therapies include virtual reality (VR) and robotic technologies. In limb rehabilitation, limb posture is required to (1) provide a limb realistic representation in VR games and (2) assess the patient improvement. When exoskeleton devices are used in the therapy, the measurements of their joint angles cannot be directly used to represent the posture of the patient limb, since the human and exoskeleton kinematic models differ. In response to this shortcoming, we propose a method to estimate the posture of the human limb attached to the exoskeleton. We use the exoskeleton joint angles measurements and the constraints of the exoskeleton on the limb to estimate the human limb joints angles. This paper presents (a) the mathematical formulation and solution to the problem, (b) the implementation of the proposed solution on a commercial exoskeleton system for the upper limb rehabilitation, (c) its integration into a rehabilitation VR game platform, and (d) the quantitative assessment of the method during elbow and wrist analytic training. Results show that this method properly estimates the limb posture to (i) animate avatars that represent the patient in VR games and (ii) obtain kinematic data for the patient assessment during elbow and wrist analytic rehabilitation.