An Architecture for Multilevel Learning and Robotic Control based on Concept Generation

Robot and multi-robot systems are inherently complex systems, for which designing the programs to control their behaviours proves complicated. Moreover, control programs that have been successfully designed for a particular environment and task can become useless if either of these change. It is for this reason that this thesis investigates the use of machine learning within robot and multi-robot systems. It explores an architecture for machine learning, applied to autonomous mobile robots based on dividing the learning task into two individual but interleaved sub-tasks. The first sub-task consists of finding an appropriate representation on which to base behaviour learning. The thesis explores the viability of using multidimensional classification techniques to generalise the original sensor and motor representations into abstract hierarchies of 'concepts'. To construct concepts the research used standard classification techniques, and experimented with a novel method of multidimensional data classification based on 'Q-analysis'. Results suggest that this may be a powerful new approach to concept learning. The second sub-task consists of using the previously acquired concepts as the representation for behaviour learning. The thesis explores whether it is possible to learn robotic behaviours represented using concepts. Results show that is possible to learn low-level behaviours such as navigation and higher-level ones such as ball passing in robot football. The thesis concludes that the proposed architecture is viable for robotic behaviour learning and control, and that incorporating Q-analysis based classification results in a promising new approach to the control of robot and multi-robot systems

Investigating balancing control of a standing bipedal robot with point foot contact

Comparing with wheeled or tracked moving machines, legged robots have potential advantages, especially when considering moving on discontinuous or rough terrain. For many bipedal robots, balance in the standing position is easy to maintain by having sufficient contact area with the ground. For some bipedal robots, the Zero Moment Point (ZMP) control method has been successfully implemented in which the center of mass is aligned above the support area. However, the balancing issue while standing becomes challenging when the contact area is very small. This paper presents a controller which is developed to balance a bipedal robot with coupled legs which has point foot contact. It is necessary to investigate the non-linear characteristics of the system. A pole-placement control method is used, and noise issues with sensing higher motion derivatives are investigated The simulation-based evaluation indicates limitations that need to be addressed before experimental implementation

Anodic stripping voltammetric determination of zinc at a 3-D printed carbon nanofiber–graphite–polystyrene electrode using a carbon pseudo-reference electrode

© 2018 Elsevier B.V. The application of a novel fully 3-D printed carbon nanofiber–graphite–polystyrene electrode has been investigated for the trace determination of Zn2+ by differential pulse anodic stripping voltammetry. The possibility of utilising a carbon pseudo-reference electrode was found to be successful. The effect of accumulation potential and time were investigated and optimised. Using an accumulation potential of −2.9 V (vs. C) and an accumulation time of 75 s a single sharp anodic stripping peak was recorded exhibiting a linear response from 12.7 μg/L to 450 μg/L. The theoretical detection limit (3σ) was calculated as 8.6 μg/L. Using the optimised conditions a mean recovery of 97.8%, (%CV = 2.0%, n = 5) for a tap water sample fortified at 0.990 μg/mL was obtained indicating the method holds promise for the determination of Zn2+ in such samples

Investigating balancing control of a standing bipedal robot with point foot contact

Comparing with wheeled or tracked moving machines, legged robots have potential advantages, especially when considering moving on discontinuous or rough terrain. For many bipedal robots, balance in the standing position is easy to maintain by having sufficient contact area with the ground. For some bipedal robots, the Zero Moment Point (ZMP) control method has been successfully implemented in which the center of mass is aligned above the support area. However, the balancing issue while standing becomes challenging when the contact area is very small. This paper presents a controller which is developed to balance a bipedal robot with coupled legs which has point foot contact. It is necessary to investigate the non-linear characteristics of the system. A pole-placement control method is used, and noise issues with sensing higher motion derivatives are investigated The simulation-based evaluation indicates limitations that need to be addressed before experimental implementation

A support vector clustering based approach for driving style classification

All drivers have their own habitual choice of driving behavior, causing variations in fuel consumption. It would be beneficial to classify these driving styles and extract the most economical and ecological driving patterns. However, driving style of each driver is not consistent and may vary within a single trip. Therefore, this paper proposes a novel technique to robustly classify driving style using the Support Vector Clustering approach, which attempts to differentiate the variations in individual's driving pattern and provides an objective driver classification. It is part of a research program aiming to replicate some humans' driving behaviors on chassis dynamometer using a robot driver. Moreover, it can potentially be used in developing more economical and personalized advanced driver assistance systems (ADAS) and humanized autonomous driving strategies. With the easily accessible on-board diagnostics (OBD) data on modern vehicles, both vehicle state and traffic information of three drivers were collected using an instrumented vehicle, which had external forward-looking radar and a monocular dashcam. For data processing, each trip data was first segmented into separate event groups. Prominent factors were then extracted by applying Principal Component Analysis (PCA) on both statistical and spectral features of all signals. Afterwards, Support Vector Clustering (SVC) was performed to classify driving style during the trip. The trained classifier was used to indicate the driving pattern variations in percentage. The validity of the proposed method was evaluated using the jerk profile, where a high correlation was found between the classification results and jerk distributions. Moreover, a positive relation between fuel consumption and driving aggressivity was also confirmed. Furthermore, it was found that weather condition, time of the day and ultimately, the driver's eagerness, can cause significant variations in driving style.</p

Closed loop control of force operation in a novel self-sensing dielectric elastomer actuator

Closed loop feedback is essential in achieving the precise control of dielectric elastomer actuators (DEAs) due to their inherent nonlinear viscoelasticity. A novel self-sensing mechanism that uses capacitive sensing to detect the actuation of force in a dielectric elastomer sensing actuator (DESA) is proposed in this paper. In contrast to a conventional self-sensing DEA, it consists of an electro-active region (AR) for the actuation together with an independent electro-sensing region (SR). By doing so, the self-sensing mechanism does not exhibit longterm drift in the correlation between the structural deformation and the capacitive change, which is commonly found in conventional self-sensing DEAs. The results show that the proportional-integral (PI) controlled DESA performs effectively under uniaxial actuation. The DESA can suppress the relaxation of the viscoelastic DE and thus enable a constant force output. It also shows that the sensing capacity of the DESA can be enhanced further with appropriate electrode arrangement and motion-constraining. Furthermore, the results show that the DESA senses the off-plane expansion distinctly compared with the in-plane deformation, which helps to detect any wrinkling of the structure