Diana Tsankova

Intelligent Sensor-Based Navigation and Control of Mobile Robot in a Partially Known Environment

Abstract Navigation and collision avoidance are major areas of research in mobile robotics that involve varying degrees of uncertainty. In this paper, a new approach is proposed for navigation and control of a wheeled mobile robot in a partially known environment. A collision-free path is calculated using an efficient neural motion planner. An additional fuzzy logic based navigation strategy is developed to adjust the moving information in the cases when the mobile robot encounters unexpected obstacles. The output of the navigation level is transformed into a time indexed data sequence which is fed into a tracking controller that takes into account the complete dynamics of the mobile base. The locomotion control structure is based on the integration of a kinematic controller and an adaptive fuzzy-net torque controller.

Intelligent Motion Planning and Control for a Simulated Mobile Robot

Abstract A complete motion planning and control procedure for mobile robot is presented. A collision-free path is calculated using a neural-net motion planner. The output of the planner is then transformed into a time indexed data sequence which is fed into a tracking controller that takes into account the complete dynamics of the mobile base. A locomotion control structure based on the integration of a kinematic controller and an adaptive fuzzy-net torque controller is proposed. An evolutionary feedback-error-learning method for automatic elicitation of knowledge in the form of fuzzy if-then rules is developed. The results of the simulations show the effectiveness of the proposed approach.

IMMUNE NAVIGATION CONTROL FOR STIGMERGY BASED FORAGING BEHAVIOUR OF AUTONOMOUS MOBILE ROBOTS

Abstract The paper presents a series of experiments in a simulated environment where two autonomous mobile robots gather randomly distributed objects and cluster them into one pile. The coordination of the robots’ movements is achieved through stigmergy (an indirect form of communication through the environment). In order to avoid the drawback of the random moves, necessary for stigmergy based foraging behaviour, the perceptive capabilities of the robots are enhanced by detectors for concentration of objects. An artificial immune network carries out the collision free goal following behaviour. Simulations confirm the improved performance of the foraging behaviour under the proposed immune navigation control.

Soft Computing Techniques in Modelling the Influence of pH and Temperature on Dopamine Biosensor

Biosensors represent very promising analytical tools that are capable of providing a continuous, fast and sensitive quantitative analysis in a straightforward and cost-effective way. According to the definition of IUPAC (International Union of Pure and Applied Chemistry) the biosensing analytical devices combine a biological element for molecular recognition with a signal-processing device (transducer). The transducer, which normally ensures the high sensitivity of the sensor, can be thermal, optical, magnetic field, piezoelectrical or electrochemical. On the other hand, the selectivity of detection is assured by the biological recognition element that might consists of either a bioligand (DNA, RNA, antibodies etc.) or a biocatalyst, such as some redox proteins, individual enzymes and enzymatic systems (cell membranes, whole microorganisms, tissues) (Castillo et al., 2004; Scheller et al. 2001). Electrochemical biosensors show two main advantages over the other types of biosensors: i) they are susceptible to miniaturization, and ii) the electrical response – current or potential, could be easily processed using not expensive and compact instrumentation. Among the electrochemical biosensors, enzyme-based amperometric biosensors represents the most used group, which functions on the basis of monitoring the current variation at an polarised electrode, induced by the reaction/interaction of the biorecognition element with the analyte of interest. Then, amperometric enzyme-based biosensors on their part, can be classified into three categories (Castillo et al., 2004; Scheller et al., 2001), in accordance with the mode of action: first generation biosensors: the signal is generated upon the electrochemical reaction of an active reagent (monitoring the decrease of the current) or product (monitoring the increase of the current) that are involved in the biochemical transformation of the target compoundthe enzyme substrate (Dimcheva et al., 2002 ; Dodevska et al., 2006; Horozova et al., 2009). second generation biosensors: the architecture of these biosensors includes a freely diffusing redox mediator (small molecular weight compounds, able to effectively shuttle electrons between the electrode surface and the enzyme active site) and in this