Generación de trayectorias para un robot móvil empleando redes neuronales

En este artículo se presenta una metodología de navegación de robots móviles en ambientes estructurados, utilizando Redes Neuronales Artificiales del tipo propagación hacia atras. Para la generación de la trayectoria optima se realiza la clasificación de superficies típicas de ambientes de robótica móvil, utilizando la base de datos obtenida de un anillo de sensores ultrasónicos. Se diseña las interfaces graficas, tanto para generar las bases de datos de las superficies, como para generar la trayectoria optima que le permita al robot desplazarse de un punto inicial a un punto final llamado objetivo.In this paper a methodology for navigation of a mobile robot in structured environments is presented. This methodology uses backpropagation neural network- to generate the optimal trajectory based of previously classification of typical surfaces of robotics environments. For the classification to use a database obtained from a ring of ultrasonic sensors. The graphical interfaces is designed, both to generate databases of surfaces, such as to generate the optimal trajectory that allows the robot to move from a starting point to goal point

Non-linear Control of an Autonomous Ground Vehicle

In this paper, in order to select a speed controller for a specific non-linear autonomous ground vehicle, proportional-integral-derivative (PID), Fuzzy, and linear quadratic regulator (LQR) controllers were designed. Here, in order to carry out the tuning of the above controllers, a multicomputer genetic algorithm (MGA) was designed. Then, the results of the MGA were used to parameterize the PID, Fuzzy and LQR controllers and to test them under laboratory conditions. Finally, a comparative analysis of the performance of the three controllers was conducted

Experimental Investigation of Obstacle-Avoiding Mobile Robots without Image Processing

A simple method to detect step height, slope angle and trench width using four PSD range sensors (GP2D12) is proposed, and the reproducibility and accuracy of characteristic parameter detection are examined. The detection error of upward slope angle is within 2.5 degrees, while that of downward slope angle exceeded 20 degrees very large. In order to reduce such errors, a range sensor that has better range-voltage performance must be introduced, or we will have to increase trial frequency to prevent detection delay. Step height can be identified with an error off ± 1.5mm. Trench width cannot be reliably measured at this time. It is suggested that an additional method is needed if we are to advance the field of obstacle detection.ELECTRICAL AND ELECTRONIC ENGINEERING, 50th anniversary editio

Navegación de robots móviles mediante comportamientos utilizando lógica difusa

Este artículo describe la implementación de comportamientos básicos de navegación de robots móviles utilizando lógica difusa. Entre los comportamientos implementados se describen el comportamiento SEGUIR PARED y el comportamiento SEGUIR PASILLO. Para la implementación de dichos comportamientos se utilizó el toolbox de lógica difusa de Matlab y el modelo cinemático de la plataforma P-METIN del Grupo GIROPS. Una de las principales características de dicha implementación es la información de distancia obtenida de los sensores de dicha plataforma

Generación de trayectorias para un robot móvil empleando redes neuronales

En este artículo se presenta una metodología de navegación de robots móviles en ambientes estructurados, utilizando Redes Neuronales Artificiales del tipo propagación hacia atrás. Para la generación de la trayectoria optima se realiza la clasificación de superficies típicas de ambientes de robótica móvil, utilizando la base de datos obtenida de un anillo de sensores ultrasónicos. Se diseña las interfaces gráficas, tanto para generar las bases de datos de las superficies, como para generar la trayectoria optima que le permita al robot desplazarse de un punto inicial a un punto final llamado objetivo

An Effective Development and Analysis of a Mobile Robot

This paper deals with the design of a battery operated Mobile Robot and development of various modes of its control. The Mobile Robot can be operated in three different modes, namely Dual Tone Multi Frequency (DTMF), Radio Frequency (RF) and ZigBee thereby enabling a multi- dimensional control system. The Mobile Robot is a single seated carrier. It can also be used to transport substantial amount of physical load for short distances. It is a prototype of a multi-use robot having a wide range of applicability according to the requirement after suitable modifications. Thus it can be easily applied inside a hospital to carry patients, as a wheel chair for physically challenged people, to carry goods in large shopping malls, as golf cars, can also be used for industrial purposes with adequate modifications. It can also be used as cleaning machine at railway stations, airports, museums, large halls. It uses a DC power source and not any conventional energy sources. Hence it is ecofriendly and this Mobile Robot can be termed as an advancing step in the field of battery operated vehicles

Vision-based Global Path Planning and Trajectory Generation for Robotic Applications in Hazardous Environments

The aim of this study is to find an efficient global path planning algorithm and trajectory generation method using a vision system. Path planning is part of the more generic navigation function of mobile robots that consists of establishing an obstacle-free path, starting from the initial pose to the target pose in the robot workspace.In this thesis, special emphasis is placed on robotic applications in industrial and scientific infrastructure environments that are hazardous and inaccessible to humans, such as nuclear power plants and ITER1 and CERN2 LHC3 tunnel. Nuclear radiation can cause deadly damage to the human body, but we have to depend on nuclear energy to meet our great demands for energy resources. Therefore, the research and development of automatic transfer robots and manipulations under nuclear environment are regarded as a key technology by many countries in the world. Robotic applications in radiation environments minimize the danger of radiation exposure to humans. However, the robots themselves are also vulnerable to radiation. Mobility and maneuverability in such environments are essential to task success. Therefore, an efficient obstacle-free path and trajectory generation method are necessary for finding a safe path with maximum bounded velocities in radiation environments. High degree of freedom manipulators and maneuverable mobile robots with steerable wheels, such as non-holonomic omni-directional mobile robots make them suitable for inspection and maintenance tasks where the camera is the only source of visual feedback.In this thesis, a novel vision-based path planning method is presented by utilizing the artificial potential field, the visual servoing concepts and the CAD-based recognition method to deal with the problem of path and trajectory planning. Unlike the majority of conventional trajectory planning methods that consider a robot as only one point, the entire shape of a mobile robot is considered by taking into account all of the robot’s desired points to avoid obstacles. The vision-based algorithm generates synchronized trajectories for all of the wheels on omni-directional mobile robot. It provides the robot’s kinematic variables to plan maximum allowable velocities so that at least one of the actuators is always working at maximum velocity. The advantage of generated synchronized trajectories is to avoid slippage and misalignment in translation and rotation movement. The proposed method is further developed to propose a new vision-based path coordination method for multiple mobile robots with independently steerable wheels to avoid mutual collisions as well as stationary obstacles. The results of this research have been published to propose a new solution for path and trajectory generation in hazardous and inaccessible to human environments where the one camera is the only source of visual feedback

Design of a cognitive neural predictive controller for mobile robot

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityIn this thesis, a cognitive neural predictive controller system has been designed to guide a nonholonomic wheeled mobile robot during continuous and non-continuous trajectory tracking and to navigate through static obstacles with collision-free and minimum tracking error. The structure of the controller consists of two layers; the first layer is a neural network system that controls the mobile robot actuators in order to track a desired path. The second layer of the controller is cognitive layer that collects information from the environment and plans the optimal path. In addition to this, it detects if there is any obstacle in the path so it can be avoided by re-planning the trajectory using particle swarm optimisation (PSO) technique. Two neural networks models are used: the first model is modified Elman recurrent neural network model that describes the kinematic and dynamic model of the mobile robot and it is trained off-line and on-line stages to guarantee that the outputs of the model will accurately represent the actual outputs of the mobile robot system. The trained neural model acts as the position and orientation identifier. The second model is feedforward multi-layer perceptron neural network that describes a feedforward neural controller and it is trained off-line and its weights are adapted on-line to find the reference torques, which controls the steady-state outputs of the mobile robot system. The feedback neural controller is based on the posture neural identifier and quadratic performance index predictive optimisation algorithm for N step-ahead prediction in order to find the optimal torque action in the transient to stabilise the tracking error of the mobile robot system when the trajectory of the robot is drifted from the desired path during transient state. Three controller methodologies were developed: the first is the feedback neural controller; the second is the nonlinear PID neural feedback controller and the third is nonlinear inverse dynamic neural feedback controller, based on the back-stepping method and Lyapunov criterion. The main advantages of the presented approaches are to plan an optimal path for itself avoiding obstructions by using intelligent (PSO) technique as well as the analytically derived control law, which has significantly high computational accuracy with predictive optimisation technique to obtain the optimal torques control action and lead to minimum tracking error of the mobile robot for different types of trajectories. The proposed control algorithm has been applied to monitor a nonholonomic wheeled mobile robot, has demonstrated the capability of tracking different trajectories with continuous gradients (lemniscates and circular) or non-continuous gradients (square) with bounded external disturbances and static obstacles. Simulations results and experimental work showed the effectiveness of the proposed cognitive neural predictive control algorithm; this is demonstrated by the minimised tracking error to less than (1 cm) and obtained smoothness of the torque control signal less than maximum torque (0.236 N.m), especially when external disturbances are applied and navigating through static obstacles. Results show that the five steps-ahead prediction algorithm has better performance compared to one step-ahead for all the control methodologies because of a more complex control structure and taking into account future values of the desired one, not only the current value, as with one step-ahead method. The mean-square error method is used for each component of the state error vector to compare between each of the performance control methodologies in order to give better control results

Environmental maps generation using LIDAR - 3D perception system

Orientador: Pablo Siqueira MeirellesTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia MecânicaResumo: Este trabalho apresenta o estudo e desenvolvimento de um Sistema de Percepção baseado na utilização de sensores telemétricos tipo LIDAR. Uma plataforma de escaneamento a laser em três dimensões LMS-3D é construída a fim da navegação autônoma de robôs. A área navegável é obtida a partir de mapas telemétricos, caracterizados com algoritmos de grades de ocupação (GO) (em duas dimensões com a terceira colorida e 3D) e com o cálculo de gradientes vetoriais. Dois tipos de áreas navegáveis são caracterizadas: (i) área de navegação primária representada por uma área livre dentro da GO; e (ii) área de navegação continua representada pela soma das áreas continuas e gradientes classificados com um determinado limiar. Este limiar indica se uma área é passível de navegação considerando as características do robô. A proposta foi avaliada experimentalmente em ambiente real, contemplou a detecção de obstáculos e a identificação de descontinuidadesAbstract: This thesis was proposed to demonstrate the study and development of a Perception System based on the utilization of a LIDAR telemetric sensors. It was proposed to create a LMS-3D three dimension laser scanning platform, in an attempt to promote the Autonomous Robot Navigation. The scanned area was obtained based on telemetric maps, which was characterized with Occupancy Grid algorithms (OG) (in two dimensions with the third colored and 3D) and Vector Gradients calculation. Two different navigation areas were characterized: (i) primary area of navigation, that represents the free area inside a OG, and (ii) continuous navigation area, that represents the navigated area composed by the sum of continuous areas and the gradients classified by a determined threshold, which indicates the possible navigated area, based on the robot characteristics. The proposition of this thesis was evaluated in a real environment and was able to identify the obstacles detection and also the discontinuanceDoutoradoMecanica dos Sólidos e Projeto MecanicoDoutor em Engenharia Mecânic