A lightweight,inexpensive robotic system for insect vision

Designing hardware for miniaturized robotics which mimics the capabilities of flying insects is of interest, because they share similar constraints (i.e. small size, low weight, and low energy consumption). Research in this area aims to enable robots with similarly efficient flight and cognitive abilities. Visual processing is important to flying insects' impressive flight capabilities, but currently, embodiment of insect-like visual systems is limited by the hardware systems available. Suitable hardware is either prohibitively expensive, difficult to reproduce, cannot accurately simulate insect vision characteristics, and/or is too heavy for small robotic platforms. These limitations hamper the development of platforms for embodiment which in turn hampers the progress on understanding of how biological systems fundamentally work. To address this gap, this paper proposes an inexpensive, lightweight robotic system for modelling insect vision. The system is mounted and tested on a robotic platform for mobile applications, and then the camera and insect vision models are evaluated. We analyse the potential of the system for use in embodiment of higher-level visual processes (i.e. motion detection) and also for development of navigation based on vision for robotics in general. Optic flow from sample camera data is calculated and compared to a perfect, simulated bee world showing an excellent resemblance.     

A GIM-Based Biomimetic Learning Approach for Motion Generation of a Multi-Joint Robotic Fish

In this paper, we propose a biomimetic learning approach for motion generation of a multi-joint robotic fish. Based on a multi-joint robotic fish model, two basic Carangiform swimming patterns, namely ＂cruise＂ and ＂C sharp turning＂, are extracted as training samples from the observations of real fish swimming. A General Internal Model （GIM）, which is an imitation of Central Pattern Generator （CPG） in nerve systems, is adopted to learn and to regenerate coordinated fish behaviors. By virtue of the universal function approximation ability and the temporal/spatial scalabilities of GIM, the proposed learning approach is able to generate the same or similar fish swimming patterns by tuning two parameters. The learned swimming patterns are implemented on a multi-joint robotic fish in experiments. The experiment results verify the effectiveness of the biomimetic learning approach in generating and modifying locomotion patterns for the robotic fish.     

A Supplementary System for a Brain-Machine Interface Based on Jaw Artifacts for the Bidimensional Control of a Robotic Arm

Non-invasive Brain-Machine Interfaces (BMIs) are being used more and more these days to design systems focused on helping people with motor disabilities. Spontaneous BMIs translate user''s brain signals into commands to control devices. On these systems, by and large, 2 different mental tasks can be detected with enough accuracy. However, a large training time is required and the system needs to be adjusted on each session. This paper presents a supplementary system that employs BMI sensors, allowing the use of 2 systems (the BMI system and the supplementary system) with the same data acquisition device. This supplementary system is designed to control a robotic arm in two dimensions using electromyographical (EMG) signals extracted from the electroencephalographical (EEG) recordings. These signals are voluntarily produced by users clenching their jaws. EEG signals (with EMG contributions) were registered and analyzed to obtain the electrodes and the range of frequencies which provide the best classification results for 5 different clenching tasks. A training stage, based on the 2-dimensional control of a cursor, was designed and used by the volunteers to get used to this control. Afterwards, the control was extrapolated to a robotic arm in a 2-dimensional workspace. Although the training performed by volunteers requires 70 minutes, the final results suggest that in a shorter period of time (45 min), users should be able to control the robotic arm in 2 dimensions with their jaws. The designed system is compared with a similar 2-dimensional system based on spontaneous BMIs, and our system shows faster and more accurate performance. This is due to the nature of the control signals. Brain potentials are much more difficult to control than the electromyographical signals produced by jaw clenches. Additionally, the presented system also shows an improvement in the results compared with an electrooculographic system in a similar environment.     

Adverse Events in Robotic Surgery: A Retrospective Study of 14 Years of FDA Data

BackgroundUse of robotic systems for minimally invasive surgery has rapidly increased during the last decade. Understanding the causes of adverse events and their impact on patients in robot-assisted surgery will help improve systems and operational practices to avoid incidents in the future.MethodsBy developing an automated natural language processing tool, we performed a comprehensive analysis of the adverse events reported to the publicly available MAUDE database (maintained by the U.S. Food and Drug Administration) from 2000 to 2013. We determined the number of events reported per procedure and per surgical specialty, the most common types of device malfunctions and their impact on patients, and the potential causes for catastrophic events such as patient injuries and deaths.ResultsDuring the study period, 144 deaths (1.4% of the 10,624 reports), 1,391 patient injuries (13.1%), and 8,061 device malfunctions (75.9%) were reported. The numbers of injury and death events per procedure have stayed relatively constant (mean = 83.4, 95% confidence interval (CI), 74.2–92.7 per 100,000 procedures) over the years. Surgical specialties for which robots are extensively used, such as gynecology and urology, had lower numbers of injuries, deaths, and conversions per procedure than more complex surgeries, such as cardiothoracic and head and neck (106.3 vs. 232.9 per 100,000 procedures, Risk Ratio = 2.2, 95% CI, 1.9–2.6). Device and instrument malfunctions, such as falling of burnt/broken pieces of instruments into the patient (14.7%), electrical arcing of instruments (10.5%), unintended operation of instruments (8.6%), system errors (5%), and video/imaging problems (2.6%), constituted a major part of the reports. Device malfunctions impacted patients in terms of injuries or procedure interruptions. In 1,104 (10.4%) of all the events, the procedure was interrupted to restart the system (3.1%), to convert the procedure to non-robotic techniques (7.3%), or to reschedule it (2.5%).ConclusionsDespite widespread adoption of robotic systems for minimally invasive surgery in the U.S., a non-negligible number of technical difficulties and complications are still being experienced during procedures. Adoption of advanced techniques in design and operation of robotic surgical systems and enhanced mechanisms for adverse event reporting may reduce these preventable incidents in the future.     

Active Sensing System with In Situ Adjustable Sensor Morphology

Background

Despite the widespread use of sensors in engineering systems like robots and automation systems, the common paradigm is to have fixed sensor morphology tailored to fulfill a specific application. On the other hand, robotic systems are expected to operate in ever more uncertain environments. In order to cope with the challenge, it is worthy of note that biological systems show the importance of suitable sensor morphology and active sensing capability to handle different kinds of sensing tasks with particular requirements.

Methodology

This paper presents a robotics active sensing system which is able to adjust its sensor morphology in situ in order to sense different physical quantities with desirable sensing characteristics. The approach taken is to use thermoplastic adhesive material, i.e. Hot Melt Adhesive (HMA). It will be shown that the thermoplastic and thermoadhesive nature of HMA enables the system to repeatedly fabricate, attach and detach mechanical structures with a variety of shape and size to the robot end effector for sensing purposes. Via active sensing capability, the robotic system utilizes the structure to physically probe an unknown target object with suitable motion and transduce the arising physical stimuli into information usable by a camera as its only built-in sensor.

Conclusions/Significance

The efficacy of the proposed system is verified based on two results. Firstly, it is confirmed that suitable sensor morphology and active sensing capability enables the system to sense different physical quantities, i.e. softness and temperature, with desirable sensing characteristics. Secondly, given tasks of discriminating two visually indistinguishable objects with respect to softness and temperature, it is confirmed that the proposed robotic system is able to autonomously accomplish them. The way the results motivate new research directions which focus on in situ adjustment of sensor morphology will also be discussed.     

High-throughput,genome-scale protein production method based on the wheat germ cell-free expression system

Current cell-free protein expression systems are capable of synthesizing proteins with high speed and accuracy; however, the yields are low due to their instability over time. Escherichia coli based systems are not always sufficient for expression of eukaryotic proteins. This report reviews a high-throughput protein production method based on the cell-free system prepared from eukaryote, wheat embryos. We first demonstrate a method for preparation of this extract that exhibited a high degree of stability and activity. To maximize translation yield and throughput, we address and resolve the following issues: (1) optimization of the ORF flanking regions; (2) PCR-based generation of DNA for mRNA production; (3) expression vectors for large-scale protein production; and (4) a translation reaction that does not require a membrane. The combination of these elemental processes with robotic automation resulted in high-throughput protein synthesis.     

Automated objective robot-assisted assessment of wrist passive ranges of motion

The measurement of wrist passive ranges of motion (ROMs) can provide insight into improvements and allow for effective monitoring during a rehabilitation program. Compared with conventional methods, this study proposed a new robotic assessment technique for measuring passive ROMs of the wrist. The robotic system has a reconfigurable handle structure that allows for multi-dimensional applications of wrist motions. The assessment reliability of this robotic system was analysed on 11 subjects for measuring wrist extension/flexion and radial/ulnar deviation. Preliminary data demonstrated its potential with intraclass correlation coefficient (ICC_2,1) all greater than 0.857 and standard error of measurement (SEM) less than 3.38°. Future work will focus on the standardization of the assessment protocol of this robotic system for assessment purposes, paving the way for its clinical application.     

Principles of goal-directed spatial robot navigation in biomimetic models

Mobile robots and animals alike must effectively navigate their environments in order to achieve their goals. For animals goal-directed navigation facilitates finding food, seeking shelter or migration; similarly robots perform goal-directed navigation to find a charging station, get out of the rain or guide a person to a destination. This similarity in tasks extends to the environment as well; increasingly, mobile robots are operating in the same underwater, ground and aerial environments that animals do. Yet despite these similarities, goal-directed navigation research in robotics and biology has proceeded largely in parallel, linked only by a small amount of interdisciplinary research spanning both areas. Most state-of-the-art robotic navigation systems employ a range of sensors, world representations and navigation algorithms that seem far removed from what we know of how animals navigate; their navigation systems are shaped by key principles of navigation in ‘real-world’ environments including dealing with uncertainty in sensing, landmark observation and world modelling. By contrast, biomimetic animal navigation models produce plausible animal navigation behaviour in a range of laboratory experimental navigation paradigms, typically without addressing many of these robotic navigation principles. In this paper, we attempt to link robotics and biology by reviewing the current state of the art in conventional and biomimetic goal-directed navigation models, focusing on the key principles of goal-oriented robotic navigation and the extent to which these principles have been adapted by biomimetic navigation models and why.     

On designing geometric motion planners to solve regulating and trajectory tracking problems for robotic locomotion systems

Based on a geometric fiber bundle structure, a generalized method to solve both regulation and trajectory tracking problems for locomotion systems is presented. The method is especially applied to two case studies of robotic locomotion systems; a three link articulated fish-like robot as a prototype of locomotion systems with symmetry, and the snakeboard as a prototype of mixed locomotion systems. Our results show that although these motion planners have an open loop structure, due to their generalities, they can steer case studies with negligible errors for almost any complicated path.     

Using Reinforcement Learning to Provide Stable Brain-Machine Interface Control Despite Neural Input Reorganization

Brain-machine interface (BMI) systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder’s neural input space (e.g. neurons appearing or being lost amongst electrode recordings). These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI) to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled.     

Automated polymerase chain reaction product sample preparation for capillary electrophoresis analysis

The analysis of crude polymerase chain reaction (PCR) products by capillary electrophoresis (CE) is often compromised due to the presence of a high concentration of salt. Salt interferes with the electrokinetic injection and induces localized heating within the column; hence, PCR products must be desalted or cleaned-up prior to CE analysis. A variety of commercial clean-up systems are available that have been traditionally used to prepare PCR products for cloning, sequencing and digestion with restriction enzymes. These systems were tested for their effectiveness in preparing PCR products for CE analysis and were evaluated based on CE resolution, salt removal, DNA recovery, processing time and cost. One particularly effective clean-up system, membrane dialysis, was automated using a robotic workstation.     

Electromyographic response is altered during robotic surgical training with augmented feedback

There is a growing prevalence of robotic systems for surgical laparoscopy. We previously developed quantitative measures to assess robotic surgical proficiency, and used augmented feedback to enhance training to reduce applied grip force and increase speed. However, there is also a need to understand the physiological demands of the surgeon during robotic surgery, and if training can reduce these demands. Therefore, the goal of this study was to use clinical biomechanical techniques via electromyography (EMG) to investigate the effects of real-time augmented visual feedback during short-term training on muscular activation and fatigue. Twenty novices were trained in three inanimate surgical tasks with the da Vinci Surgical System. Subjects were divided into five feedback groups (speed, relative phase, grip force, video, and control). Time- and frequency-domain EMG measures were obtained before and after training. Surgical training decreased muscle work as found from mean EMG and EMG envelopes. Grip force feedback further reduced average and total muscle work, while speed feedback increased average muscle work and decreased total muscle work. Training also increased the median frequency response as a result of increased speed and/or reduced fatigue during each task. More diverse motor units were recruited as revealed by increases in the frequency bandwidth post-training. We demonstrated that clinical biomechanics using EMG analysis can help to better understand the effects of training for robotic surgery. Real-time augmented feedback during training can further reduce physiological demands. Future studies will investigate other means of feedback such as biofeedback of EMG during robotic surgery training.     

Organ printing: from bioprinter to organ biofabrication line

Organ printing, or the layer by layer additive robotic biofabrication of functional three-dimensional tissue and organ constructs using self-assembling tissue spheroid building blocks, is a rapidly emerging technology that promises to transform tissue engineering into a commercially successful biomedical industry. It is increasingly obvious that similar well-established industries implement automated robotic systems on the path to commercial translation and economic success. The use of robotic bioprinters alone however is not sufficient for the development of large industrial scale organ biofabrication. The design and development of a fully integrated organ biofabrication line is imperative for the commercial translation of organ printing technology. This paper presents recent progress and challenges in the development of the essential components of an organ biofabrication line.     

Comparison of Robotic Surgery with Laparoscopy and Laparotomy for Treatment of Endometrial Cancer: A Meta-Analysis

Purpose

To compare the relative merits among robotic surgery, laparoscopy, and laparotomy for patients with endometrial cancer by conducting a meta-analysis.

Methods

The MEDLINE, Embase, PubMed, Web of Science, and Cochrane Library databases were searched. Studies clearly documenting a comparison between robotic surgery and laparoscopy or between robotic surgery and laparotomy for endometrial cancer were selected. The outcome measures included operating time (OT), number of complications, length of hospital stay (LOHS), estimated blood loss (EBL), number of transfusions, total lymph nodes harvested (TLNH), and number of conversions. Pooled odds ratios and weighted mean differences with 95% confidence intervals were calculated using either a fixed-effects or random-effects model.

Results

Twenty-two studies were included in the meta-analysis. These studies involved a total of 4420 patients, 3403 of whom underwent both robotic surgery and laparoscopy and 1017 of whom underwent both robotic surgery and laparotomy. The EBL (p = 0.01) and number of conversions (p = 0.0008) were significantly lower and the number of complications (p<0.0001) was significantly higher in robotic surgery than in laparoscopy. The OT, LOHS, number of transfusions, and TLNH showed no significant differences between robotic surgery and laparoscopy. The number of complications (p<0.00001), LOHS (p<0.00001), EBL (p<0.00001), and number of transfusions (p = 0.03) were significantly lower and the OT (p<0.00001) was significantly longer in robotic surgery than in laparotomy. The TLNH showed no significant difference between robotic surgery and laparotomy.

Conclusions

Robotic surgery is generally safer and more reliable than laparoscopy and laparotomy for patients with endometrial cancer. Robotic surgery is associated with significantly lower EBL than both laparoscopy and laparotomy; fewer conversions but more complications than laparoscopy; and shorter LOHS, fewer complications, and fewer transfusions but a longer OT than laparoscopy. Further studies are required.     

Robot Guided ‘Pen Skill’ Training in Children with Motor Difficulties

Motor deficits are linked to a range of negative physical, social and academic consequences. Haptic robotic interventions, based on the principles of sensorimotor learning, have been shown previously to help children with motor problems learn new movements. We therefore examined whether the training benefits of a robotic system would generalise to a standardised test of ‘pen-skills’, assessed using objective kinematic measures [via the Clinical Kinematic Assessment Tool, CKAT]. A counterbalanced, cross-over design was used in a group of 51 children (37 male, aged 5–11 years) with manual control difficulties. Improved performance on a novel task using the robotic device could be attributed to the intervention but there was no evidence of generalisation to any of the CKAT tasks. The robotic system appears to have the potential to support motor learning, with the technology affording numerous advantages. However, the training regime may need to target particular manual skills (e.g. letter formation) in order to obtain clinically significant improvements in specific skills such as handwriting.     

Fly-like visuomotor responses of a robot using aVLSI motion-sensitive chips

 We explore the use of continuous-time analog very-large-scale-integrated (aVLSI) neuromorphic visual preprocessors together with a robotic platform in generating bio-inspired behaviors. Both the aVLSI motion sensors and the robot behaviors described in this work are inspired by the motion computation in the fly visual system and two different fly behaviors. In most robotic systems, the visual information comes from serially scanned imagers. This restricts the form of computation of the visual image and slows down the input rate to the controller system of the robot, hence increasing the reaction time of the robot. These aVLSI neuromorphic sensors reduce the computational load and power consumption of the robot, thus making it possible to explore continuous-time visuomotor control systems that react in real-time to the environment. The motion sensor provides two outputs: one for the preferred direction and the other for the null direction. These motion outputs are created from the aggregation of six elementary motion detectors that implement a variant of Reichardt's correlation algorithm. The four analog continuous-time outputs from the motion chips go to the control system on the robot which generates a mixture of two behaviors – course stabilization and fixation – from the outputs of these sensors. Since there are only four outputs, the amount of information transmitted to the controller is reduced (as compared to using a CCD sensor), and the reaction time of the robot is greatly decreased. In this work, the robot samples the motion sensors every 3.3 ms during the behavioral experiments. Received: 4 October 1999 / Accepted in revised form: 26 April 2001     

An adaptive gaze stabilization controller inspired by the vestibulo-ocular reflex

The vestibulo-ocular reflex stabilizes vision in many vertebrates. It integrates inertial and visual information to drive the eyes in the opposite direction to head movement and thereby stabilizes the image on the retina. Its adaptive nature guarantees stable vision even when the biological system undergoes dynamic changes (due to disease, growth or fatigue etc), a characteristic especially desirable in autonomous robotic systems. Based on novel, biologically plausible neurological models, we have developed a robotic testbed to qualitatively evaluate the performance of these algorithms. We show how the adaptive controller can adapt to a time varying plant and elaborate how this biologically inspired control architecture can be employed in general engineering applications where sensory feedback is very noisy and/or delayed.     

Invariant ankle moment patterns when walking with and without a robotic ankle exoskeleton

To guide development of robotic lower limb exoskeletons, it is necessary to understand how humans adapt to powered assistance. The purposes of this study were to quantify joint moments while healthy subjects adapted to a robotic ankle exoskeleton and to determine if the period of motor adaptation is dependent on the magnitude of robotic assistance. The pneumatically powered ankle exoskeleton provided plantar flexor torque controlled by the wearer's soleus electromyography (EMG). Eleven naïve individuals completed two 30-min sessions walking on a split-belt instrumented treadmill at 1.25 m/s while wearing the ankle exoskeleton. After two sessions of practice, subjects reduced their soleus EMG activation by ～36% and walked with total ankle moment patterns similar to their unassisted gait (r²=0.98±0.02, THSD, p>0.05). They had substantially different ankle kinematic patterns compared to their unassisted gait (r²=0.79±0.12, THSD, p<0.05). Not all of the subjects reached a steady-state gait pattern within the two sessions, in contrast to a previous study using a weaker robotic ankle exoskeleton (Gordon and Ferris, 2007). Our results strongly suggest that humans aim for similar joint moment patterns when walking with robotic assistance rather than similar kinematic patterns. In addition, greater robotic assistance provided during initial use results in a longer adaptation process than lesser robotic assistance.     

Distributed scalable multi-robot learning using particle swarm optimization

Designing effective behavioral controllers for mobile robots can be difficult and tedious; this process can be circumvented by using online learning techniques which allow robots to generate their own controllers online in an automated fashion. In multi-robot systems, robots operating in parallel can potentially learn at a much faster rate by sharing information amongst themselves. In this work, we use an adapted version of the Particle Swarm Optimization algorithm in order to accomplish distributed online robotic learning in groups of robots with access to only local information. The effectiveness of the learning technique on a benchmark task (generating high-performance obstacle avoidance behavior) is evaluated for robot groups of various sizes, with the maximum group size allowing each robot to individually contain and manage a single PSO particle. To increase the realism of the technique, different PSO neighborhoods based on limitations of real robotic communication are tested and compared in this scenario. We explore the effect of varying communication power for one of these communication-based PSO neighborhoods. To validate the effectiveness of these learning techniques, fully distributed online learning experiments are run using a group of 10 real robots, generating results which support the findings from our simulations.