Design of a quadrotor micro air vehicle

In this paper, we present the design and development details of a micro air vehicle (MAV) built around a quadrotor configuration. A survey of implemented MAVs suggests that a quadrotor design has several advantages over other configurations, especially in the context of swarm intelligence applications. Our design approach consists of three stages. However, the focus of this paper is restricted to the first stage that involves selection of crucial components such as motor-rotor pair, battery source, and structural material. The application of MAVs are broad-ranging, from reconnaissance to search and rescue, and have immense potential in the rapidly advancing field of swarm intelligence.

Plane wave analysis of rectangular axial-inlet, axial-outlet and transverse-inlet, transverse-outlet air cleaners

In this work, one-dimensional flow-acoustic analysis of two basic configurations of air cleaners, (i) Rectangular Axial-Inlet, Axial-Outlet (RAIAO) and (ii) Rectangular Transverse-Inlet, Transverse-Outlet (RTITO), has been presented. This 1-D analytical approach has been verified with the help of 3-D FEM based software. Through subtraction of the acoustic performance of the bare plenum (without filter element) from that of the complete air cleaner box, the solitary performance of the filter element has been evaluated. Part of the present analysis illustrates that the analytical formulation remains effective even with offset positioning of the air pipes from the centre of the cross section of the air cleaner. The 1-D analytical tool computes much faster than its 3-D simulation counterpart. The present analysis not only predicts the acoustical impact of mean flow, but it also depicts the scenario with increased resistance of the filter element. Thus, the proposed 1-D analysis would help in the design of acoustically efficient air cleaners for automotive applications. (C) 2011 Institute of Noise Control Engineering.

Robust Fixed Order Lateral H2 Controller for Micro Air Vehicle

This paper presents a robust fixed order H2controller design using strengthened discrete optimal projection equations, which approximate the first order necessary optimality condition. The novelty of this work is the application of the robust H2controller to a micro aerial vehicle named Sarika2 developed in house. The controller is designed in discrete domain for the lateral dynamics of Sarika2 in the presence of low frequency atmospheric turbulence (gust) and high frequency sensor noise. The design specification includes simultaneous stabilization, disturbance rejection and noise attenuation over the entire flight envelope of the vehicle. The resulting controller performance is comprehensively analyzed by means of simulation

Modeling urban traffic planning thro' strategic decisions- A case from Bangalore, India

This paper deals with reducing the waiting times of vehicles at the traffic junctions by synchronizing the traffic signals. Strategies are suggested for betterment of the situation at different time intervals of the day, thus ensuring smooth flow of traffic. The concept of single way systems are also analyzed. The situation is simulated in Witness 2003 Simulation package using various conventions. The average waiting times are reduced by providing an optimal combination for the traffic signal timer. Different signal times are provided for different times of the day, thereby further reducing the average waiting times at specific junctions/roads according to the experienced demands.

Reinforcement learning with average cost for adaptive control of traffic lights at intersections

We propose for the first time two reinforcement learning algorithms with function approximation for average cost adaptive control of traffic lights. One of these algorithms is a version of Q-learning with function approximation while the other is a policy gradient actor-critic algorithm that incorporates multi-timescale stochastic approximation. We show performance comparisons on various network settings of these algorithms with a range of fixed timing algorithms, as well as a Q-learning algorithm with full state representation that we also implement. We observe that whereas (as expected) on a two-junction corridor, the full state representation algorithm shows the best results, this algorithm is not implementable on larger road networks. The algorithm PG-AC-TLC that we propose is seen to show the best overall performance.

Tribology of ethylene-air diffusion flame soot under dry and lubricated contact conditions

Soot particles are generated in a flame caused by burning ethylene gas. The particles are collected thermophoretically at different locations of the flame. The particles are used to lubricate a steel/steel ball on flat reciprocating sliding contact, as a dry solid lubricant and also as suspended in hexadecane. Reciprocating contact is shown to establish a protective and low friction tribo-film. The friction correlates with the level of graphitic order of the soot, which is highest in the soot extracted from the mid-flame region and is low in the soot extracted from the flame root and flame tip regions. Micro-Raman spectroscopy of the tribo-film shows that the a priori graphitic order, the molecular carbon content of the soot and the graphitization of the film as brought about by tribology distinguish between the frictions of soot extracted from different regions of the flame, and differentiate the friction associated with dry tribology from that recorded under lubricated tribology.

Fuzzy inference based data preprocessing for VBR video traffic prediction

Prediction of variable bit rate compressed video traffic is critical to dynamic allocation of resources in a network. In this paper, we propose a technique for preprocessing the dataset used for training a video traffic predictor. The technique involves identifying the noisy instances in the data using a fuzzy inference system. We focus on three prediction techniques, namely, linear regression, neural network and support vector regression and analyze their performance on H.264 video traces. Our experimental results reveal that data preprocessing greatly improves the performance of linear regression and neural network, but is not effective on support vector regression.