Bridging ROS for Heterogeneous Integration in Mobile Robot Systems

We investigate the difficulty of integrating disparate, heterogeneous systems which have not been designed to work together. Such difficulties may arise from differences in communication protocols or data formats, making an in- tegration effort largely manual and labor intensive. The investigation is done in the context of integrating two different robot systems, one mobile platform running ROS (Robot Operating System) and one stationary two-armed ABB robot. The thesis consists of two parts. First, existing solutions to this problem (or parts of it) are examined and evaluated for their applicability. After no suitable solution is found, a tool is then created which solves the problem of integrating non-ROS compatible devices with a ROS system. The presented tool is a program that generates modular bridging nodes between ROS and other systems. Finally, the tool proves its value in the integration of two different robots, where one system also receives some additional changes for practical reasons

The Amoeba Distributed Operating System - A Status Report

As the price of CPU chips continues to fall rapidly, it will soon be economically feasible to build computer systems containing a large number of processors. The question of how this computing power should be organized, and what kind of operating system is appropriate then arises. Our research during the past decade has focused on these issues and led to the design of a distributed operating system, called Amoeba, that is intended for systems with large numbers of computers. In this paper we describe Amoeba, its philosophy, its design, its applications, and some experience with it. 1

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Agent Based E-Market: Framework, Design, and Implementation

Attempt has been made to design and develop a complete adoptive Multi Agent System pertaining to merchant brokering stage of Customer Buying Behaviour Model with the intent of appropriate framework. Intelligent agents are autonomous entity which observe and act upon an environment. In general, they are software robots and vitally used in variety of e-Business applications. This paper focuses on the discussions on electronic markets and the adoptive role, which agents can play in information transformation for automating e-market transactions. It is proposed to develop a framework for agent-based electronic markets for buyers and sellers totally with the assistance of software agents.Agent Oriented e-Business, Agent Oriented e-Markets, Buyer/Seller Agents, Java, Multi Agent Systems

Embedding quasi-static time series within a genetic algorithm for stochastic optimization: the case of reactive power compensation on distribution systems

This paper presents a methodology for the optimal placement and sizing of reactive power compensation devices in a distribution system (DS) with distributed generation. Quasi-static time series is embedded in an optimization method based on a genetic algorithm to adequately represent the uncertainty introduced by solar photovoltaic generation and electricity demand and its effect on DS operation. From the analysis of a typical DS, the reactive power compensation rating power results in an increment of 24.9% when compared to the classical genetic algorithm model. However, the incorporation of quasi-static time series analysis entails an increase of 26.8% on the computational time required