Route training in mobile robots through system identification

Fundamental sensor-motor couplings form the backbone of most mobile robot control tasks, and often need to be implemented fast, efficiently and nevertheless reliably. Machine learning techniques are therefore often used to obtain the desired sensor-motor competences. In this paper we present an alternative to established machine learning methods such as artificial neural networks, that is very fast, easy to implement, and has the distinct advantage that it generates transparent, analysable sensor-motor couplings: system identification through nonlinear polynomial mapping. This work, which is part of the RobotMODIC project at the universities of Essex and Sheffield, aims to develop a theoretical understanding of the interaction between the robot and its environment. One of the purposes of this research is to enable the principled design of robot control programs. As a first step towards this aim we model the behaviour of the robot, as this emerges from its interaction with the environment, with the NARMAX modelling method (Nonlinear, Auto-Regressive, Moving Average models with eXogenous inputs). This method produces explicit polynomial functions that can be subsequently analysed using established mathematical methods. In this paper we demonstrate the fidelity of the obtained NARMAX models in the challenging task of robot route learning; we present a set of experiments in which a Magellan Pro mobile robot was taught to follow four different routes, always using the same mechanism to obtain the required control law

Accurate robot simulation through system identification

Robot simulators are useful tools for developing robot behaviours. They provide a fast and efficient means to test robot control code at the convenience of the office desk. In all but the simplest cases though, due to the complexities of the physical systems modelled in the simulator, there are considerable differences between the behaviour of the robot in the simulator and that in the real world environment. In this paper we present a novel method to create a robot simulator using real sensor data. Logged sensor data is used to construct a mathematically explicit model(in the form of a NARMAX polynomial) of the robot’s environment. The advantage of such a transparent model — in contrast to opaque modelling methods such as artificial neural networks — is that it can be analysed to characterise the modelled system, using established mathematical methods In this paper we compare the behaviour of the robot running a particular task in both the simulator and the real-world using qualitative and quantitative measures including statistical methods to investigate the faithfulness of the simulator

Visual task identification and characterisation using polynomial models

Developing robust and reliable control code for autonomous mobile robots is difficult, because the interaction between a physical robot and the environment is highly complex, subject to noise and variation, and therefore partly unpredictable. This means that to date it is not possible to predict robot behaviour based on theoretical models. Instead, current methods to develop robot control code still require a substantial trial-and-error component to the software design process. This paper proposes a method of dealing with these issues by a) establishing task-achieving sensor-motor couplings through robot training, and b) representing these couplings through transparent mathematical functions that can be used to form hypotheses and theoretical analyses of robot behaviour. We demonstrate the viability of this approach by teaching a mobile robot to track a moving football and subsequently modelling this task using the NARMAX system identification technique

Robot programming by demonstration through system identification

Increasingly, personalised robots — robots especially designed and programmed for an individual’s needs and preferences — are being used to support humans in their daily lives, most notably in the area of service robotics. Arguably, the closer the robot is programmed to the individual’s needs, the more useful it is, and we believe that giving people the opportunity to program their own robots, rather than programming robots for them, will push robotics research one step further in the personalised robotics field. However, traditional robot programming techniques require specialised technical skills from different disciplines and it is not reasonable to expect end-users to have these skills. In this paper, we therefore present a new method of obtaining robot control code — programming by demonstration through system identification which algorithmically and automatically transfers human behaviours into robot control code, using transparent, analysable mathematical functions. Besides providing a simple means of generating perception-action mappings, they have the additional advantage that can also be used to form hypotheses and theoretical analysis of robot behaviour. We demonstrate the viability of this approach by teaching a Scitos G5 mobile robot to achieve wall following and corridor passing behaviours

Comparing robot controllers through system identification

In the mobile robotics field, it is very common to find different control programs designed to achieve a particular robot task. Although there are many ways to evaluate these controllers qualitatively, there is a lack of formal methodology to compare them from a mathematical point of view. In this paper we present a novel approach to compare robot control codes quantitatively based on system identification: Initially the transparent mathematical models of the controllers are obtained using the NARMAX system identification process. Then we use these models to analyse the general characteristics of the cotrollers from a mathematical point of view. In this way, we are able to compare different control programs objectively based on quantitative measures. We demonstrate our approach by comparing two different robot control programs, which were designed to drive the robot through door-like openings

Learning by observation through system identification

In our previous works, we present a new method to program mobile robots —“code identification by demonstration”— based on algorithmically transferring human behaviours to robot control code using transparent mathematical functions. Our approach has three stages: i) first extracting the trajectory of the desired behaviour by observing the human, ii) making the robot follow the human trajectory blindly to log the robot’s own perception perceived along that trajectory, and finally iii) linking the robot’s perception to the desired behaviour to obtain a generalised, sensor-based model. So far we used an external, camera based motion tracking system to log the trajectory of the human demonstrator during his initial demonstration of the desired motion. Because such tracking systems are complicated to set up and expensive, we propose an alternative method to obtain trajectory information, using the robot’s own sensor perception. In this method, we train a mathematical polynomial using the NARMAX system identification methodology which maps the position of the “red jacket” worn by the demonstrator in the image captured by the robot’s camera, to the relative position of the demonstrator in the real world according to the robot. We demonstrate the viability of this approach by teaching a Scitos G5 mobile robot to achieve door traversal behaviour

Accuracy of reflectance photoplethysmography on detecting cuff-induced vascular occlusions

Photoplethysmography (PPG) is a noninvasive optical technique, which can also be used to derive important parameters other than arterial oxygen saturation (SpO2). In this work, the accuracy of the technique on detecting changes in blood perfusion during different levels of vascular occlusions has been explored. A dual-wavelength, reflectance PPG probe was applied on the left forearm of 10 healthy volunteers and raw PPG signals were acquired by a research PPG processing system. The raw PPG signals were separated into pulsatile AC and continuous DC PPG components. The signals were used to estimate SpO2 and changes in concentration of oxygenated, deoxygenated, and total haemoglobin. Different levels of occlusions, from 20 mmHg to total occlusion were induced by a pressure-cuff on the left arm. The system was able to indicate all the occlusions. In particular, the haemoglobin concentration changes estimated from PPG were in high agreement with Near Infrared Spectroscopy measurements

Investigation of Photoplethysmography and Near Infrared Spectroscopy for the Assessment of Tissue Blood Perfusion

Pulse Oximetry (PO) and Near Infrared Spectroscopy (NIRS) are among the most widely adopted optical techniques for the assessment of tissue perfusion. PO estimates arterial oxygen saturation (SpO2) by exploiting light attenuations due to pulsatile arterial blood (AC) and constant absorbers (DC) at two different wavelengths. NIRS processes the attenuations of at least two wavelengths to calculate concentrations of Deoxygenated ([HHb]), Oxygenated ([HbO2]), Total Haemoglobin ([tHb]) and Tissue Oxygenation Index (TOI). In this work we present the development and evaluation of a reflectance PPG probe and processing system for the assessment of tissue perfusion. The system adopts both Pulse Oximetry and NIRS principles to calculate SpO2, [HHb], and [HbO2] and [tHb]. The system has been evaluated on the forearm of 10 healthy volunteers during cuff-induced vascular occlusions. The presented system was able to estimate SpO2, [HHb], [HbO2] and [tHb], showing good agreement with state-of-the-art NIRS and conventional PO

#hayfever; A Longitudinal Study into Hay Fever Related Tweets in the UK

This paper describes a longitudinal study that has collected and analysed over 512,000 UK geolocated tweets over 2 years from June 2012 that contained instances of the words “hayfever” and “hay fever”. The results indicate that the temporal distribution of the tweets collected in 2014 correlates strongly (r=0.97, p<0.01) with incidents of hay fever reported by the Royal College of General Practitioners (RCGP) in the same year. An analysis of the content of the tweets indicates that users are self-reporting common, often severe symptoms as well as the uses of medication. We conclude that hay fever related tweets provide a real-time, free and easily accessible source of data at a finer level of granularity than currently available data sets. The implications for researchers, health professionals and sufferers are also discussed

Reflectance Photoplethysmography as Non-Invasive Monitoring of Tissue Blood Perfusion.

In the last decades Photoplethysmography (PPG) has been used as noninvasive technique for monitoring arterial oxygen saturation by Pulse Oximetry (PO), whereas Near Infrared Spectroscopy (NIRS) has been employed for monitoring tissue blood perfusion. While NIRS offers more parameters to evaluate oxygen delivery and consumption in deep tissues, PO only assesses the state of oxygen delivery. For a broader assessment of blood perfusion, this paper explores the utilization of dual-wavelength PPG by using the pulsatile (AC) and continuous (DC) PPG for the estimation of arterial oxygen saturation (SpO2) by conventional PO. Additionally, the Beer-Lambert law is applied to the DC components only for the estimation of changes in deoxy-hemoglobin (HHb), oxy-hemoglobin (HbO2) and total hemoglobin (tHb) as in NIRS. The system was evaluated on the forearm of 21 healthy volunteers during induction of venous occlusion (VO) and total occlusion (TO). A reflectance PPG probe and NIRS sensor were applied above the brachioradialis, PO sensors were applied on the fingers, and all the signals were acquired simultaneously. While NIRS and forearm SpO2 indicated VO, SpO2 from the finger did not exhibit any significant drop from baseline. During TO all the indexes indicated the change in blood perfusion. HHb, HbO2 and tHb changes estimated by PPG presented high correlation with the same parameters obtained by NIRS during VO (r2=0.960, r2=0.821 and r2 =0.974 respectively) and during TO (r2=0.988, r2=0.940 and r2=0.938 respectively). The system demonstrated the ability to extract valuable information from PPG signals for a broader assessment of tissue blood perfusion