Towards modeling complex robot training tasks through system identification

Previous research has shown that sensor-motor tasks in mobile robotics applications can be modelled automatically, using NARMAX system identi�cation, where the sensory perception of the robot is mapped to the desired motor commands using non-linear polynomial functions, resulting in a tight coupling between sensing and acting | the robot responds directly to the sensor stimuli without having internal states or memory. However, competences such as for instance sequences of actions, where actions depend on each other, require memory and thus a representation of state. In these cases a simple direct link between sensory perception and the motor commands may not be enough to accomplish the desired tasks. The contribution to knowledge of this paper is to show how fundamental, simple NARMAX models of behaviour can be used in a bootstrapping process to generate complex behaviours that were so far beyond reach. We argue that as the complexity of the task increases, it is important to estimate the current state of the robot and integrate this information into the system identification process. To achieve this we propose a novel method which relates distinctive locations in the environment to the state of the robot, using an unsupervised clustering algorithm. Once we estimate the current state of the robot accurately, we combine the state information with the perception of the robot through a bootstrapping method to generate more complex robot tasks: We obtain a polynomial model which models the complex task as a function of predefined low level sensor motor controllers and raw sensory data. The proposed method has been used to teach Scitos G5 mobile robots a number of complex tasks, such as advanced obstacle avoidance, or complex route learning

Robot training using system identification

This paper focuses on developing a formal, theory-based design methodology to generate transparent robot control programs using mathematical functions. The research finds its theoretical roots in robot training and system identification techniques such as Armax (Auto-Regressive Moving Average models with eXogenous inputs) and Narmax (Non-linear Armax). These techniques produce linear and non-linear polynomial functions that model the relationship between a robot’s sensor perception and motor response. The main benefits of the proposed design methodology, compared to the traditional robot programming techniques are: (i) It is a fast and efficient way of generating robot control code, (ii) The generated robot control programs are transparent mathematical functions that can be used to form hypotheses and theoretical analyses of robot behaviour, and (iii) It requires very little explicit knowledge of robot programming where end-users/programmers who do not have any specialised robot programming skills can nevertheless generate task-achieving sensor-motor couplings. The nature of this research is concerned with obtaining sensor-motor couplings, be it through human demonstration via the robot, direct human demonstration, or other means. The viability of our methodology has been demonstrated by teaching various mobile robots different sensor-motor tasks such as wall following, corridor passing, door traversal and route learning

Complex robot training tasks through bootstrapping system identification

Many sensor-motor competences in mobile robotics applications exhibit complex, non-linear characteristics. Previous research has shown that polynomial NARMAX models can learn such complex tasks. However as the complexity of the task under investigation increases, representing the whole relationship in one single model using only raw sensory inputs would lead to large models. Training such models is extremely difficult, and, furthermore, obtained models often exhibit poor performances. This paper presents a bootsrapping method of generating complex robot training tasks using simple NARMAX models. We model the desired task by combining predefined low level sensor motor controllers. The viability of the proposed method is demonstrated by teaching a Scitos G5 autonomous robot to achieve complex route learning tasks in the real world robotics experiments

An application of lyapunov stability analysis to improve the performance of NARMAX models

Previously we presented a novel approach to program a robot controller based on system identification and robot training techniques. The proposed method works in two stages: first, the programmer demonstrates the desired behaviour to the robot by driving it manually in the target environment. During this run, the sensory perception and the desired velocity commands of the robot are logged. Having thus obtained training data we model the relationship between sensory readings and the motor commands of the robot using ARMAX/NARMAX models and system identification techniques. These produce linear or non-linear polynomials which can be formally analysed, as well as used in place of “traditional robot” control code. In this paper we focus our attention on how the mathematical analysis of NARMAX models can be used to understand the robot’s control actions, to formulate hypotheses and to improve the robot’s behaviour. One main objective behind this approach is to avoid trial-and-error refinement of robot code. Instead, we seek to obtain a reliable design process, where program design decisions are based on the mathematical analysis of the model describing how the robot interacts with its environment to achieve the desired behaviour. We demonstrate this procedure through the analysis of a particular task in mobile robotics: door traversal

Hybrid sorbent-ultrafiltration systems for the removal of hormones and fluoride from water

The presence of trace contaminants in drinking water resources has been related to adverse health effects in living organisms and humans. Current technologies do not adequately remove these contaminants from water and /or require high energy supply. Exploring low cost, low energy processes in order to eliminate trace contaminants is essential considering that access to clean drinking water and energy is becoming more challenging in many parts of the world. Hormones and fluoride are the two contaminants studied in this research and hybrid systems which combine sorption with low pressure ultrafiltration are proposed for their removal. Sorption is a promising removal mechanism if efficient sorbents and operational conditions are selected, however, the introduction of sorbent materials can cause fouling in ultrafiltration. Fouling reduces the membrane permeability and increases the energy requirement of the system. The overall aim is to study the proposed hybrid sorbent- ultrafiltration systems in terms of contaminant sorption capacity and membrane performance. The systems are tested under varying sorbent size (52 -3000 nm for hormone, <38 -500 μm for fluoride removal) sorbent concentration (1.7 -84 mg /L for hormone, 1 -50 g/L for fluoride removal), sorbate concentration (100 ng /L hormone and 5 -500 mg /L fluoride) and solution pH (3 -12). The thesis can be split into two parts: one part for hormones and the other for fluoride.In the first part, a hybrid polystyrene nanoparticle -ultrafiltration system is investigated for hormone removal. Polystyrene nanoparticles are employed as they provide a large active surface area for the sorption and they can easily be manufactured in different sizes and with various functional groups. The results show that the system can only compete with the existing nanofiltration/reverse osmosis membrane systems if the sorption capacity of the polystyrene nanoparticles is increased. For this reason, carboxyl functionalized polystyrene nanoparticles were also tested. Contrary to expectations, even less hormone sorption is achieved with the functionalized particles. Further investigation of other functional groups such as amine /amidine for their hormone sorption capacity is recommended.In the second part, laterite and bone char are selected as two sorbents for the hybrid sorbent -UF system for fluoride removal as they are locally sourced, low cost materials in parts of Ghana and Tanzania, respectively, where fluoride contamination is a major problem. The sorption capacity and the membrane fouling of the hybrid system with the two selected sorbents are compared. Fluoride sorption capacity of the bone char system is higher than the laterite system and this is attributed to the difference in the available surface area. The fouling of the membranes operated with laterite at high initial fluoride concentrations and alkaline solutions is linked to the precipitation of iron and aluminium complexes. With further system optimization, both hybrid laterite and bone char systems show the potential to be viable solutions for fluoride removal, noting that the bone char system is more feasible for high fluoride concentrations above 10 mg /L. Based on lab scale experimental results, two hybrid laterite -ultrafiltration systems are designed to be tested in Ghana. The two systems, one with submerged hollow fibre and the other with direct dead end tubular ultrafiltration membrane modules, are operated with real surface and ground waters. The findings indicate that the amount of sorption obtained in the field is lower than that which is obtained with laboratory experiments due to the presence of interfering co -ions in the real waters and differences in membrane systems. The systems also show the potential to remove arsenic, uranium and lead. The system with hollow fibre membranes can be suggested as an appropriate system for ground water applications as it did not experience any fouling and the investment cost could be lower compared to the tubular membranes. However, if the surface waters are to be treated with the proposed hybrid system, the tubular membranes offers a system with no fouling. The hybrid laterite -UF system shows to be a promising treatment technology for fluoride contaminated waters in Ghana

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

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

Fish Swimming in a Kármán Vortex Street:Kinematics, Sensory Biology and Energetics

Fishes often live in environments characterized by complex flows. To study the mechanisms of how fishes interact with unsteady flows, the periodic shedding of vortices behind cylinders has been employed to great effect. In particular, fishes that hold station in a vortex street (i.e., K?rm?n gaiting) show swimming kinematics that are distinct from their patterns of motion during freestream swimming in uniform flows, although both behaviors can be modeled as an undulatory body wave. K?rm?n gait kinematics are largely preserved across flow velocities. Larger fish have a shorter body wavelength and slower body wave speed than smaller fish, in contrast to freestream swimming where body wavelength and wave speed increases with size. The opportunity for K?rm?n gaiting only occurs under specific conditions of flow velocity and depends on the length of the fish; this is reflected in the highest probability of K?rm?n gaiting at intermediate flow velocities. Fish typically K?rm?n gait in a region of the cylinder wake where the velocity deficit is about 40% of the nominal flow. The lateral line plays a role in tuning the kinematics of the K?rm?n gait, since blocking it leads to aberrant kinematics. Vision allows fish to maintain a consistent position relative to the cylinder. In the dark, fish do not show the same preference to hold station behind a cylinder though K?rm?n gait kinematics are the same. When oxygen consumption level is measured, it reveals that K?rm?n gaiting represents about half of the cost of swimming in the freestreamauthorsversionPeer reviewe