Consumer robotic products: studying the factors that influence market adoption

The aim of this article is to identify the key factors that are associated with the adoption of a commercial robot in the home. This article is based on the development of the robot product Cybot by the University of Reading in conjunction with a publisher (Eaglemoss International Ltd.). The robots were distributed through a new part-work magazine series (Ultimate Real Robots) that had long-term customer usage and retention. A part-work is a serial publication that is issued periodically (e.g., every two weeks), usually in magazine format, and builds into a complete collection. This magazine focused on robotics and was accompanied by cover-mounted component parts that could be assembled, with instructions, by the user to build a working robot over the series. In total, the product contributed over half a million operational domestic robots to the world market, selling over 20 million robot part-work magazines across 18 countries, thereby providing a unique breadth of insight. Gaining a better understanding of the overall attitudes that customers of this product had toward robots in the home, their perception of what such devices could deliver and how they would wish to interact with them should provide results applicable to the domestic appliance, assistance/care, entertainment, and educational markets.

Extended Kalman filtering for robotic proximity sensing

The authors address the problems in using a fiber-optic proximity sensor to detect robot end-effector positioning errors in performing a contact task when uncertainties about target position exist. An extended Kalman filter approach is employed to solve the nonlinear filtering problem. Some experimental results are given.

Modular non-computational-connectionist-hybrid neural network approach to robotic systems

Spiking neural networks are usually limited in their applications due to their complex mathematical models and the lack of intuitive learning algorithms. In this paper, a simpler, novel neural network derived from a leaky integrate and fire neuron model, the ‘cavalcade’ neuron, is presented. A simulation for the neural network has been developed and two basic learning algorithms implemented within the environment. These algorithms successfully learn some basic temporal and instantaneous problems. Inspiration for neural network structures from these experiments are then taken and applied to process sensor information so as to successfully control a mobile robot.

Developing skin analogues for a robotic octopus

In order to fabricate a biomimetic skin for an octopus inspired robot, a new process was developed based on mechanical properties measured from real octopus skin. Various knitted nylon textiles were tested and the one of 10-denier nylon was chosen as reinforcement. A combination of Ecoflex 0030 and 0010 silicone rubbers was used as matrix of the composite to obtain the right stiffness for the skin-analogue system. The open mould fabrication process developed allows air bubble to escape easily and the artificial skin produced was thin and waterproof. Material properties of the biomimetic skin were characterised using static tensile and instrumented scissors cutting tests. The Young’s moduli of the artificial skin are 0.08 MPa and 0.13 MPa in the longitudinal and transverse directions, which are much lower than those of the octopus skin. The strength and fracture toughness of the artificial skin, on the other hand are higher than those of real octopus skins. Conically-shaped skin prototypes to be used to cover the robotic arm unit were manufactured and tested. The biomimetic skin prototype was stiff enough to maintain it conical shape when filled with water. The driving force for elongation was reduced significantly compared with previous prototypes.

Cloud droplet size and liquid water path retrievals from zenith radiance measurements: examples from the Atmospheric Radiation Measurement Program and the Aerosol Robotic Network

The ground-based Atmospheric Radiation Measurement Program (ARM) and NASA Aerosol Robotic Net- work (AERONET) routinely monitor clouds using zenith ra- diances at visible and near-infrared wavelengths. Using the transmittance calculated from such measurements, we have developed a new retrieval method for cloud effective droplet size and conducted extensive tests for non-precipitating liquid water clouds. The underlying principle is to combine a liquid-water-absorbing wavelength (i.e., 1640 nm) with a non-water-absorbing wavelength for acquiring information on cloud droplet size and optical depth. For simulated stratocumulus clouds with liquid water path less than 300 g m−2 and horizontal resolution of 201 m, the retrieval method underestimates the mean effective radius by 0.8μm, with a root-mean-squared error of 1.7 μm and a relative deviation of 13%. For actual observations with a liquid water path less than 450 g m−2 at the ARM Oklahoma site during 2007– 2008, our 1.5-min-averaged retrievals are generally larger by around 1 μm than those from combined ground-based cloud radar and microwave radiometer at a 5-min temporal resolution. We also compared our retrievals to those from combined shortwave flux and microwave observations for relatively homogeneous clouds, showing that the bias between these two retrieval sets is negligible, but the error of 2.6 μm and the relative deviation of 22 % are larger than those found in our simulation case. Finally, the transmittance-based cloud effective droplet radii agree to better than 11 % with satellite observations and have a negative bias of 1 μm. Overall, the retrieval method provides reasonable cloud effective radius estimates, which can enhance the cloud products of both ARM and AERONET.

Robotic multiwell planar patch-clamp for native and primary mammalian cells

Robotic multiwell planar patch-clamp has become common in drug development and safety programs because it enables efficient and systematic testing of compounds against ion channels during voltage-clamp. It has not, however, been adopted significantly in other important areas of ion channel research, where conventional patch-clamp remains the favored method. Here, we show the wider potential of the multiwell approach with the ability for efficient intracellular solution exchange, describing protocols and success rates for recording from a range of native and primary mammalian cells derived from blood vessels, arthritic joints and the immune and central nervous systems. The protocol involves preparing a suspension of single cells to be dispensed robotically into 4-8 microfluidic chambers each containing a glass chip with a small aperture. Under automated control, giga-seals and whole-cell access are achieved followed by preprogrammed routines of voltage paradigms and fast extracellular or intracellular solution exchange. Recording from 48 chambers usually takes 1-6 h depending on the experimental design and yields 16-33 cell recordings.

A feasible study of EEG-driven assistive robotic system for stroke rehabilitation

Stroke is a medical emergency and can cause a neurological damage, affecting the motor and sensory systems. Harnessing brain plasticity should make it possible to reconstruct the closed loop between the brain and the body, i.e., association of the generation of the motor command with the somatic sensory feedback might enhance motor recovery. In order to aid reconstruction of this loop with a robotic device it is necessary to assist the paretic side of the body at the right moment to achieve simultaneity between motor command and feedback signal to somatic sensory area in brain. To this end, we propose an integrated EEG-driven assistive robotic system for stroke rehabilitation. Depending on the level of motor recovery, it is important to provide adequate stimulation for upper limb motion. Thus, we propose an assist arm incorporating a Magnetic Levitation Joint that can generate a compliant motion due to its levitation and mechanical redundancy. This paper reports on a feasibility study carried out to verify the validity of the robot sensing and on EEG measurements conducted with healthy volunteers while performing a spontaneous arm flexion/extension movement. A characteristic feature was found in the temporal evolution of EEG signal in the single motion prior to executed motion which can aid in coordinating timing of the robotic arm assistance onset.

Benchmarks and the accuracy of GARCH model estimation

This paper reviews nine software packages with particular reference to their GARCH model estimation accuracy when judged against a respected benchmark. We consider the numerical consistency of GARCH and EGARCH estimation and forecasting. Our results have a number of implications for published research and future software development. Finally, we argue that the establishment of benchmarks for other standard non-linear models is long overdue.

Visual sensor fusion for active security in robotic industrial environments

This work presents a method of information fusion involving data captured by both a standard CCD camera and a ToF camera to be used in the detection of the proximity between a manipulator robot and a human. Both cameras are assumed to be located above the work area of an industrial robot. The fusion of colour images and time of light information makes it possible to know the 3D localization of objects with respect to a world coordinate system. At the same time this allows to know their colour information. Considering that ToF information given by the range camera contains innacuracies including distance error, border error, and pixel saturation, some corrections over the ToF information are proposed and developed to improve the results. The proposed fusion method uses the calibration parameters of both cameras to reproject 3D ToF points, expressed in a common coordinate system for both cameras and a robot arm, in 2D colour images. In addition to this, using the 3D information, the motion detection in a robot industrial environment is achieved, and the fusion of information is applied to the foreground objects previously detected. This combination of information results in a matrix that links colour and 3D information, giving the possibility of characterising the object by its colour in addition to its 3D localization. Further development of these methods will make it possible to identify objects and their position in the real world, and to use this information to prevent possible collisions between the robot and such objects.

A review of existing building benchmarks and the development of a set of reference office buildings for England and Wales

The modern built environment has become more complex in terms of building types, environmental systems and use profiles. This complexity causes difficulties in terms of optimising buildings energy design. In this circumstance, introducing a set of prototype reference buildings, or so called benchmark buildings, that are able to represent all or majority parts of the UK building stock may be useful for the examination of the impact of national energy policies on building energy consumption. This study proposes a set of reference office buildings for England and Wales based on the information collected from the Non-Domestic Building Stock (NDBS) project and an intensive review of the existing building benchmarks. The proposed building benchmark comprises 10 prototypical reference buildings, which in relation to built form and size, represent 95% of office buildings in England and Wales. This building benchmark provides a platform for those involved in building energy simulations to evaluate energy-efficiency measures and for policy-makers to assess the influence of different building energy policies.

Visual sensor fusion for active security in robotic industrial environments

This work presents a method of information fusion involving data captured by both a standard charge-coupled device (CCD) camera and a time-of-flight (ToF) camera to be used in the detection of the proximity between a manipulator robot and a human. Both cameras are assumed to be located above the work area of an industrial robot. The fusion of colour images and time-of-flight information makes it possible to know the 3D localization of objects with respect to a world coordinate system. At the same time, this allows to know their colour information. Considering that ToF information given by the range camera contains innacuracies including distance error, border error, and pixel saturation, some corrections over the ToF information are proposed and developed to improve the results. The proposed fusion method uses the calibration parameters of both cameras to reproject 3D ToF points, expressed in a common coordinate system for both cameras and a robot arm, in 2D colour images. In addition to this, using the 3D information, the motion detection in a robot industrial environment is achieved, and the fusion of information is applied to the foreground objects previously detected. This combination of information results in a matrix that links colour and 3D information, giving the possibility of characterising the object by its colour in addition to its 3D localisation. Further development of these methods will make it possible to identify objects and their position in the real world and to use this information to prevent possible collisions between the robot and such objects.

Proposal of a floating mechanism based on magnetic levitation method toward EEG-driven robotic system

We discussed a floating mechanism based on quasi-magnetic levitation method that can be attached at the endpoint of a robot arm in order to construct a novel redundant robot arm for producing compliant motions. The floating mechanism can be composed of magnets and a constraint mechanism such that the repelling force of the magnets floats the endpoint part of the mechanism stable for the guided motions. The analytical and experimental results show that the proposed floating mechanism can produce stable floating motions with small inertia and viscosity. The results also show that the proposed mechanism can detect small force applied to the endpoint part because the friction force of the mechanism is very small.

How do I know if my forecasts are better? Using benchmarks in Hydrological ensemble prediction

The skill of a forecast can be assessed by comparing the relative proximity of both the forecast and a benchmark to the observations. Example benchmarks include climatology or a naïve forecast. Hydrological ensemble prediction systems (HEPS) are currently transforming the hydrological forecasting environment but in this new field there is little information to guide researchers and operational forecasters on how benchmarks can be best used to evaluate their probabilistic forecasts. In this study, it is identified that the forecast skill calculated can vary depending on the benchmark selected and that the selection of a benchmark for determining forecasting system skill is sensitive to a number of hydrological and system factors. A benchmark intercomparison experiment is then undertaken using the continuous ranked probability score (CRPS), a reference forecasting system and a suite of 23 different methods to derive benchmarks. The benchmarks are assessed within the operational set-up of the European Flood Awareness System (EFAS) to determine those that are ‘toughest to beat’ and so give the most robust discrimination of forecast skill, particularly for the spatial average fields that EFAS relies upon. Evaluating against an observed discharge proxy the benchmark that has most utility for EFAS and avoids the most naïve skill across different hydrological situations is found to be meteorological persistency. This benchmark uses the latest meteorological observations of precipitation and temperature to drive the hydrological model. Hydrological long term average benchmarks, which are currently used in EFAS, are very easily beaten by the forecasting system and the use of these produces much naïve skill. When decomposed into seasons, the advanced meteorological benchmarks, which make use of meteorological observations from the past 20 years at the same calendar date, have the most skill discrimination. They are also good at discriminating skill in low flows and for all catchment sizes. Simpler meteorological benchmarks are particularly useful for high flows. Recommendations for EFAS are to move to routine use of meteorological persistency, an advanced meteorological benchmark and a simple meteorological benchmark in order to provide a robust evaluation of forecast skill. This work provides the first comprehensive evidence on how benchmarks can be used in evaluation of skill in probabilistic hydrological forecasts and which benchmarks are most useful for skill discrimination and avoidance of naïve skill in a large scale HEPS. It is recommended that all HEPS use the evidence and methodology provided here to evaluate which benchmarks to employ; so forecasters can have trust in their skill evaluation and will have confidence that their forecasts are indeed better.

Obstacle avoidance routing scheme through optimal sink movement for home monitoring and mobile robotic consumer devices

In recent years, ZigBee has been proven to be an excellent solution to create scalable and flexible home automation networks. In a home automation network, consumer devices typically collect data from a home monitoring environment and then transmit the data to an end user through multi-hop communication without the need for any human intervention. However, due to the presence of typical obstacles in a home environment, error-free reception may not be possible, particularly for power constrained devices. A mobile sink based data transmission scheme can be one solution but obstacles create significant complexities for the sink movement path determination process. Therefore, an obstacle avoidance data routing scheme is of vital importance to the design of an efficient home automation system. This paper presents a mobile sink based obstacle avoidance routing scheme for a home monitoring system. The mobile sink collects data by traversing through the obstacle avoidance path. Through ZigBee based hardware implementation and verification, the proposed scheme successfully transmits data through the obstacle avoidance path to improve network performance in terms of life span, energy consumption and reliability. The application of this work can be applied to a wide range of intelligent pervasive consumer products and services including robotic vacuum cleaners and personal security robots1.

Development of a wearable assistive soft robotic device for elbow rehabilitation

The loss of motor function at the elbow joint can result as a consequence of stroke. Stroke is a clinical illness resulting in long lasting neurological deficits often affecting somatosensory and motor cortices. More than half of those that recover from a stroke survive with disability in their upper arm and need rehabilitation therapy to help in regaining functions of daily living. In this paper, we demonstrated a prototype of a low-cost, ultra-light and wearable soft robotic assistive device that could aid administration of elbow motion therapies to stroke patients. In order to assist the rotation of the elbow joint, the soft modules which consist of soft wedge-like cellular units was inflated by air to produce torque at the elbow joint. Highly compliant rotation can be naturally realised by the elastic property of soft silicone and pneumatic control of air. Based on the direct visual-actuation control, a higher control loop utilised visual processing to apply positional control, the lower control loop was implemented by an electronic circuit to achieve the desired pressure of the soft modules by Pulse Width Modulation. To examine the functionality of the proposed soft modular system, we used an anatomical model of the upper limb and performed the experiments with healthy participants.