Adriano Carvalho

Cooperative multi-robot systems:: A study of vision-based 3-D mapping using information theory

Building cooperatively 3-D maps of unknown environments is one of the application fields of multi-robot systems. This article introduces a distributed architecture, within a probabilistic framework for vision-based 3-D mapping, whereby each robot is committed to cooperate with other robots through information sharing. An entropy-based measure of information utility is defined, which a robot uses for communicating to its teammates the most useful measurements, thus preventing the robot to overwhelm communication resources with redundant information. Experiments with real robots, equipped with stereo-vision, yielded important conclusions about the way robots should cooperate on sharing information.

Cooperative Multi-Robot Systems A study of Vision-based 3-D Mapping using Information Theory

Building cooperatively 3-D maps of unknown environments is one of the application fields of multi-robot systems. This article introduces a distributed architecture, within a probabilistic framework for vision-based 3-D mapping, whereby each robot is committed to cooperate with other robots through information sharing. An entropy-based measure of information utility is defined, which a robot uses for communicating to its teammates the most useful measurements, thus preventing the robot to overwhelm communication resources with redundant information. Experiments with real robots, equipped with stereo-vision, yielded important conclusions about the way robots should cooperate on sharing information.

Exploring information theory for vision-based volumetric mapping

This article presents an innovative probabilistic approach for building volumetric maps of unknown environments with autonomous mobile robots, which is based on information theory. Each mobile robot uses an entropy gradient-based exploration strategy, with the aim of maximizing information gain when building and improving a 3D map upon measurements yielded by an on-board stereo-vision sensor. The proposed framework was validated through experiments with a real mobile robot equipped with stereo-vision, in order to be further used on cooperative volumetric mapping with teams of mobile robots.

Parameter Identification of Power Semiconductor Device Models Using Metaheuristics

Parameter extraction procedures for power semiconductor models are a need for researchers working with development of power circuits. It is nowadays recognized that an identification procedure is crucial in order to design power circuits easily through simulation (Allard et al., 2003; Claudio et al., 2002; Kang et al., 2003c; Lauritzen et al., 2001). Complex or inaccurate parameterization often discourages design engineers from attempting to use physics-based semiconductor models in their circuit designs. This issue is particularly relevant for IGBTs because they are characterized by a large number of parameters. Since IGBT models developed in recent years lack an identification procedure, different recent papers in literature address this issue (Allard et al., 2003; Claudio et al., 2002; Hefner & Bouche, 2000; Kang et al., 2003c; Lauritzen et al., 2001). Different approaches have been taken, most of them cumbersome to be solved since they are very complex and require so precise measurements that are not useful for usual needs of simulation. Manual parameter identification is still a hard task and some effort is necessary to match experimental and simulated results. A promising approach is to combine standard extraction methods to get an initial satisfying guess and then use numerical parameter optimization to extract the optimum parameter set (Allard et al., 2003; Bryant et al., 2006; Chibante et al., 2009b). Optimization is carried out by comparing simulated and experimental results from which an error value results. A new parameter set is then generated and iterative process continues until the parameter set converges to the global minimum error. The approach presented in this chapter is based in (Chibante et al., 2009b) and uses an optimization algorithm to perform the parameter extraction: the Simulated Annealing (SA) algorithm. The NPT-IGBT is used as case study (Chibante et al., 2008; Chibante et al., 2009b). In order to make clear what parameters need to be identified the NPT-IGBT model and the related ADE solution will be briefly present in following sections. 1

A model for battery lifetime calculation implementable in circuit simulators

For pure electrical propelled vehicles, PEVs, a proper battery model is critical in order to estimate State of Charge, SOC, therefore enabling predicting performance and ensuring safe battery operation. Present methods can be classified as electrochemical, electrical equivalent circuits, stochastic and analytical, these last ones based on solution of Partial Differential Equations. These methods are somehow computationally intensive, prejudicing their application in Battery Management Systems. Some ones do not accurately predict battery SOC and most of them are not applicable in electrical circuit simulators. This paper presents a model, based on Ficks laws, capable of an accurate prevision of battery lifetime, i.e. time until battery is empty, and implementable in any circuit simulator. Proposed model behaviour is analysed using PSIM and MATLAB and validated with experimental results.

Neural Network Modelling And Simulation Of The Scheduling

A three layer feed forward neural network was constructed and tested to analyze the scheduling process on single machine. The operating variables studied are the operation, processing time, setup time, deadline time, duedate time, priority, machine, and fabric color. These variables were used as input to the constructed neural network in order to predict the scheduling completion time as the output on a single machine. Three layer feed forward network trained with error back propagation learning rule are used. The constructed network was found to be precise in modeling the scheduling for the operating conditions studied and also, in predicting the scheduling for the new input data which are kept unaware of the trained neural network.

A new FPGA based PMSM torque controller for hybrid and electric vehicles powertrain

This paper presents a new approach to vector PMSM control implemented on a FPGA platform with a soft processor core integration for floating point computation. An advanced vector control is developed, according to PMSM vector control state-of-the-art, where the torque reference is a direct input to the system, dynamically controlling the injection of stator current flux component, in order to direct control of the torque angle. This approach provides a more robust solution for electric and hybrid electric vehicles as the controlled variable is the torque and the controller actuates simultaneously on the flux and torque current components and their angle. State-of-the-art presents two different control methods based on stator current flux component injection and the authors discuss another two methods, considering different relations between air-gap flux, rotor flux and stator current vectors. These methods are described and one is shown as appropriate to electric vehicle powertrain applications, regarding performance, efficiency and motor parameters. Concerning motor position information feedback, a PLL-based sensorless control technique is presented and compared with a solution based on position measurement. A hardware and software prototype based on a Xilinx® Spartan-6 FPGA and an electric vehicle powertrain set up are developed and tested for performance validation and experimental results are presented. The results show a robust controller with a high dynamics response.

Solving a real job shop scheduling problem

This work deals with a real job shop scheduling problem (jssp). The operations sequences, processing time, setup time, deadline time, due date time, priority, machine, fabric color and related technological decisions data are obtained from orders database along with a set of technological criteria about machines, which allows automatically analyzing and generating the initial scheduling sequences. These sequences are processed later by two procedures, one based on Ant Colony, and the other in Genetic Algorithms. Both procedures are integrated to a generative scheduling method and dispatching rules for big problems. In this approach is integrated technological information from: the process planning and scheduling process, in order to improve the processing time and the solution quality.

High dimensional fuzzy controller for an UPS parallel system

Fuzzy logic as a basis for control method for nonlinear control systems is introduced. Multi-input/multi-output (MIMO) controllers are complex structures. Some of the main issues to be handled in a MIMO controller are practical limitations in memory space and processing time. In consequence, the design of a multivariable controller should consider aspects like the hierarchical controller structure, the input variables grouping, the existing cross coupling, or the composition method to be adopted. The paper describes two approaches, developed for a special configuration of the input variables, for designing a MIMO fuzzy controller. The parallel operation of uninterruptible power supplies (UPS) is described as a target application showing its MIMO characteristic, with high order transfer functions, high cross coupling, and containing parameters with some level of uncertainty. So, it is an appropriate system to validate the application of fuzzy logic theory as a basis for developing a nonlinear control method. Global simulation results, for real operating conditions and for both the designed controllers are presented. Comparative results state the relative performance of the two controllers. Experimental results for the two controller types, in the supervision of two UPSs in parallel operation, are presented and their relative complexity and performance are discussed.