Areolino de Almeida Neto

Accumulative Learning using Multiple ANN for Flexible Link Control

This paper presents a scheme of multiple neural networks (MNNs) with a new strategy of combination. This combination can obtain an accumulative learning: the knowledge is increased by gradually adding more neural networks to the system. This scheme is applied to flexible link control via feedback-error-learning (FEL) strategy, here called multi-network-feedback-error-learning. Three different neural control approaches are used to control a flexible link, and it is shown that a better inverse dynamic model of the plant is obtained in this case.

Optimization of Traffic Lights Timing Based on Artificial Neural Networks

This paper presents a neural networks based traffic light controller for urban traffic road intersection called EOM-ANN Controller (Environment Observation Method based on Artificial Neural Networks Controller). EOM is a very interesting mathematical method for determining traffic lights timing. However, this method has some implications which artificial neural networks were proposed to improve such problems. To evaluate the proposed traffic control system, an isolated intersection was built in simulation software named SUMO (Simulation of Urban Mobility).

Optimization of Traffic Lights Timing Based on Multiple Neural Networks

This paper presents a neural networks based traffic light controller for urban traffic road intersection called EOM-MNN Controller (Environment Observation Method based on Multiple Neural Networks Controller). Traffic congestion leads to problems like delays and higher fuel consumption. Consequently, alleviating congested situation is not only good to economy but also to environment. The problem of traffic light control is very challenging. Traditional mathematical methods have some limitations when they are applied in traffic control. Thus, modern artificial intelligent ways have gained more and more attentions. In this work, EOM is a very interesting mathematical method for determining traffic lights timing that was developed by Ejzenberg [4]. However, this method has some implications in which multiple neural networks were proposed to improve such problems. The solution was compared with the conventional method through scenario of simulation in microscopic traffic simulation software.

FastSLAM filter implementation for indoor autonomous robot

In this paper, we present a FastSLAM particle filter algorithm used to efficiently map large indoor environments features. The proposed filter uses an unknown data association to match the extracted environment characteristics, such as walls and doors. Data association (DA) is chosen due to two reasons: 1) permit to rearrange the filter particles in the prediction phase of the filter, and 2) enable to incorporate the extracted features in the map of each particle. Indoor SLAM experiments were conducted in a long corridor composed by several wooden walls. These provisional walls were used to create a more challenging environment. From the map obtained by the mapping process, the robot is capable of navigating through the environment using the set of 22 predefined poses. The SLAM filter measurements are compared with their actual measured values.

Obstacle Avoidance in Dynamic Environment: a Hierarchical Solution

This article presents a concept for obstacle avoidance in dynamic environment suitable for mobile robot. The task of obstacle avoidance is divided in three principal groups: local, global and for emergencies. The local avoidance is here approached, in which the concept used is based on reinforcement learning, in such a way that the situations are divided into four states and two kinds of actions are possible. The states define in what situation the movement relationship between the robot and the dynamic obstacles is present, and the actions decide in which direction the robot must follow, in order to avoid a possible collision. And besides, here it is showed also how the state-action matrix was filled and its representation using neural network.

Autonomous feature-based exploration using a low-cost mobile robot

This article is concerned with the solution of the SLAM (Simultaneous Localization And Mapping) problem in a medium scale indoor environment using a low-cost mobile robot that autonomously explores the environment. The low-cost robot was built with a distance measurement subsystem composed of three types of sensors: a wireless webcam with a laser pointer (a visual sensor), two infrared sensors and an ultrasonic sensor. SLAM experiments were performed in small and medium scale environments where the robot operated autonomously. This article shows the results of a SLAM experiment in 55 m long by 2.8 m wide corridor where several artificial walls were used to simulate a more complex environment. The acquired map closely matches the real environment and is also used to navigate the robot.

Multi-Network-Feedback-Error-Learning in pelletizing plant control

This work is devoted to present a control application in an industrial process of iron pellet cooking in an important mining company in Brazil. This work uses an adaptive control in order to improve the performance of the conventional controller already installed in the plant. The main strategy approached here is known Multi-Network-Feedback-Error-Learning (MNFEL), it uses multiple neural networks in the strategy Feedback-Error-Learning (FEL).

Multi-Network-Feedback-Error-Learning with Automatic Insertion

This work is devoted to present a control application in an industrial process of iron pellet cooking in an important mining company in Brazil. This work employs an adaptive control in order to improve the performance of the conventional controller already installed in the plant. The main strategy approached here is known as Multi-Network-Feedback-Error-Learning (MNFEL). The basic idea in MNFEL is the progressive addition of neural networks in the Feedback-Error-Learning (FEL) scheme. However, this work brings innovation by proposing a mechanism of automatic insertion of new neural networks in MNFEL. In this work, due to the unknown mathematic model of the iron pellet cooking, the plant is simulated by a previously learned neural model. In such simulation environment, the proposed method is compared against conventional PID, FEL and MNFEL.

Iterative Decentralized Planning for Collective Construction Tasks with Quadrotors

This paper describes an iterative decentralized planning and learning method, based on stochastic learning automata theory and heuristic search techniques, to generate construction and motion strategies to build different types of three-dimensional structures using multiple quadrotors. This architecture is proposed to simultaneously solve three main problems: 1) the iterative generation of feasible construction and motion plans for each quadrotor; 2) the optimization with constraints on power and assembly while taking into account the dynamic nature of the environment, and 3) the planning of the translational speeds and selection of breakpoints for each vehicle. The quadrotors learn the optimal action policy to construct the structures while avoiding collisions during the loading and unloading procedures. In order to demonstrate the generality of the solution, simulated trials of the proposed autonomous construction system are presented where different three-dimensional structures are built.

A New Particle Weighting Strategy for Robot Mapping FastSLAM.

Nowadays, FastSLAM filters are the most widely used methods to solve the Simultaneous Localization and Mapping (SLAM) problem. In general, these approaches can use complex matrix formulation for computing the particle weighting procedure, during the execution of the SLAM algorithm. In this paper, we describe a new particle weight strategy for the FastSLAM filter, which can maintain the generation of particles in its most simplified form. Thus, this approach tries to estimate the robot poses and build the environment map using a simple geometric formulation for executing the particle weighting procedure. This method is capable of reducing the processing time and keeping the accuracy of the robot pose. Both simulation and experimental results demonstrate the feasibility of the proposed approach at enabling a robotic vehicle to accomplish the mapping of an unknown environment and also navigate through it.