Neural network control of a simple mobile robot

In recent years researchers in the Department of Cybernetics have been developing simple mobile robots capable of exploring their environment on the basis of the information obtained from a few simple sensors. These robots are used as the test bed for exploring various behaviours of single and multiple organisms: the work is inspired by considerations of natural systems. In this paper we concentrate on that part of the work which involves neural networks and related techniques. These neural networks are used both to process the sensor information and to develop the strategy used to control the robot. Here the robots, their sensors, and the neural networks used and all described. 1.

Simulation of a diffusion driven mobile robot

In this paper, we investigate the possibility to control a mobile robot via a sensory-motory coupling utilizing diffusion system. For this purpose, we implemented a simulation of the diffusion process of chemicals and the kinematics of the mobile robot. In comparison to the original Braitenberg vehicle in which sensorymotor coupling is tightly realised by hardwiring, our system employs the soft coupling. The mobile robot has two sets of independent sensory-motor unit, two sensors are implemented in front and two motors on each side of the robot. The framework used for the sensory-motor coupling was such that 1) Place two electrodes in the medium 2) Drop a certain amount of Chemical U and V related to the distance to the walls and the intensity of the light 3) Place other two electrodes in the medium 4) Measure the concentration of Chemical U and V to actuate the motors on both sides of the robot. The environment was constructed with four surrounding walls and a light source located at the center. Depending on the design parameters and initial conditions, the robot was able to successfully avoid the wall and light. More interestingly, the diffusion process in the sensory-motor coupling provided the robot with a simple form of memory which would not have been possible with a control framework based on a hard-wired electric circuit.

Localization of a mobile robot based on odometry and natural landmarks using extended kalman filter

SANTANA, André M.; SOUZA, Anderson A. S.; BRITTO, Ricardo S.; ALSINA, Pablo J.; MEDEIROS, Adelardo A. D. Localization of a mobile robot based on odometry and natural landmarks using extended Kalman Filter. In: INTERNATIONAL CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS, 5., 2008, Funchal, Portugal. Proceedings... Funchal, Portugal: ICINCO, 2008.

Paraconsistent Autonomous Mobile Robot Emmy III

This work presents some improvements regarding to the autonomous mobile robot Emmy based on Paraconsistent Annotated Evidential Logic ET. A discussion on navigation system is presented.

JSEG-based image segmentation in computer vision for agricultural mobile robot navigation

This project aims to apply image processing techniques in computer vision featuring an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained.

ANN statistical image recognition method for computer vision in agricultural mobile robot navigation

The main application area in this project, is to deploy image processing and segmentation techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. Thereby, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for image recognition. Hence, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave computational platforms, along with the application of customized Back-propagation Multilayer Perceptron (MLP) algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of segmented images in which reasonably accurate results were obtained. © 2010 IEEE.

Environment for teaching and development of mobile robot systems

Using robots for teaching is one approach that has gathered good results on Middle-School, High-School and Universities. Robotics gives chance to experiment concepts of a broad range of disciplines, principally those from Engineering courses and Computer Science. However, there are not many kits that enables the use of robotics in classroom. This article describes the methodologies to implement tools which serves as test beds for the use of robotics to teach Computer Science and Engineering. Therefore, it proposes the development of a flexible, low cost hardware to integrate sensors and control actuators commonly found on mobile robots, the development of a mobile robot device whose sensors and actuators allows the experimentation of different concepts, and an environment for the implementation of control algorithms through a computer network. This paper describes each one of these tools and discusses the implementation issues and future works. © 2010 IEEE.

Pattern recognition structured heuristics methods for image processing in mobile robot navigation

In this project, the main focus is to apply image processing techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained. ©2010 IEEE.

Mobile Robot navigation modelling, control and applications

This work presents and discusses the main topics involved on the design of a mobile robot system and focus on the control and navigation systems for autonomous mobile robots. Introduces the main aspects of the Robot design, which is a holistic vision about all the steps of the development process of an autonomous mobile robot; discusses the problems addressed to the conceptualization of the mobile robot physical structure and its relation to the world. Presents the dynamic and control analysis for navigation robots with kinematic and dynamic model and, for final, presents applications for a robotic platform of Automation, Simulation, Control and Supervision of Mobile Robots Navigation, with studies of dynamic and kinematic modelling, control algorithms, mechanisms for mapping and localization, trajectory planning and the platform simulator. © 2012 Praise Worthy Prize S.r.l. - All rights reserved.

A proposed neural control for the trajectory tracking of a nonholonomic mobile robot with disturbances

In this paper, a trajectory tracking control problem for a nonholonomic mobile robot by the integration of a kinematic neural controller (KNC) and a torque neural controller (TNC) is proposed, where both the kinematic and dynamic models contains disturbances. The KNC is a variable structure controller (VSC) based on the sliding mode control theory (SMC), and applied to compensate the kinematic disturbances. The TNC is a inertia-based controller constituted of a dynamic neural controller (DNC) and a robust neural compensator (RNC), and applied to compensate the mobile robot dynamics, and bounded unknown disturbances. Stability analysis with basis on Lyapunov method and simulations results are provided to show the effectiveness of the proposed approach. © 2012 Springer-Verlag.

Development of a support vector machine-based navigation system for driving a mobile robot through paths in plantations

The use of mobile robots turns out to be interesting in activities where the action of human specialist is difficult or dangerous. Mobile robots are often used for the exploration in areas of difficult access, such as rescue operations and space missions, to avoid human experts exposition to risky situations. Mobile robots are also used in agriculture for planting tasks as well as for keeping the application of pesticides within minimal amounts to mitigate environmental pollution. In this paper we present the development of a system to control the navigation of an autonomous mobile robot through tracks in plantations. Track images are used to control robot direction by preprocessing them to extract image features. Such features are then submitted to a support vector machine in order to find out the most appropriate route. The overall goal of the project to which this work is connected is to develop a real time robot control system to be embedded into a hardware platform. In this paper we report the software implementation of a support vector machine, which so far presented around 93% accuracy in predicting the appropriate route. © 2012 IEEE.