Multi-Agent Rendezvous Algorithm with Rectilinear Decision Domain

The aim of this paper is to develop a computationally efficient decentralized rendezvous algorithm for a group of autonomous agents. The algorithm generalizes the notion of sensor domain and decision domain of agents to enable implementation of simple computational algorithms. Specifically, the algorithm proposed in this paper uses a rectilinear decision domain (RDD) as against the circular decision domain assumed in earlier work. Because of this, the computational complexity of the algorithm reduces considerably and, when compared to the standard Ando's algorithm available in the literature, the RDD algorithm shows very significant improvement in convergence time performance. Analytical results to prove convergence and supporting simulation results are presented in the paper.

Positional Consensus of Multi-Agent Systems Using Linear Programming Based Decentralized Control with Rectilinear Decision Domain

In this paper we develop a Linear Programming (LP) based decentralized algorithm for a group of multiple autonomous agents to achieve positional consensus. Each agent is capable of exchanging information about its position and orientation with other agents within their sensing region. The method is computationally feasible and easy to implement. Analytical results are presented. The effectiveness of the approach is illustrated with simulation results.

Goal Seeking for Robots in Unknown Environments

We consider the problem of goal seeking by robots in unknown environments. We present a frontier based algorithm for finding a route to a goal in a fully unknown environment, where information about the goal region (GR), the region where the goal is most likely to be located, is available. Our algorithm efficiently chooses the best candidate frontier cell, which is on the boundary between explored space and unexplored space, having the maximum ``goal seeking index'', to reach the goal in minimal number of moves. Modification of the algorithm is also proposed to further reduce the number of moves toward the goal. The algorithm has been tested extensively in simulation runs and results demonstrate that the algorithm effectively directs the robot to the goal and completes the search task in minimal number of moves in bounded as well as unbounded environments. The algorithm is shown to perform as well as a state of the art agent centered search algorithm RTAA*, in cluttered environments if exact location of the goal is known at the beginning of the mission and is shown to perform better in uncluttered environments.

Microwave assisted synthesis and UV-Vis spectroscopic studies of silver nanoparticles synthesized using vanillin as a reducing agent

The present investigation explores the adaptability of a microwave assisted route to obtain silver nanoparticles by the reduction of AgNO3 with vanillin, an environmentally benign material. Anionic surfactants such as AOT and SDS were used separately for encapsulating AgNPs and their role was compared. The UV-Visible absorption spectra present a broad SPR band consisting of two peaks suggesting the formation of silver nanoparticle with bimodal size distribution. The TEM image shows particles with spherical and hexagonal morphologies which confirms the results of UV-Vis studies. The anisotropy in the particle morphology can be attributed to the surface oxidation which in turn produces Ag@Ag2O core-shell nanostructures. Thus an intriguing feature of this system is that the obtained colloid is a mixture of AgNPs with and without Ag2O layers. Studies on the influence of pH on the stability of the synthesized nanoparticles revealed that the presence of excess Ag2O layers has a profound influence on it. Ag2O layers can be removed from AgNPs' surface by changing the solution pH to the acidic regime. The present study attests the enhanced ability of AOT in stabilizing the AgNPs in aqueous media. (C) 2011 Elsevier B.V. All rights reserved.

Automated Multi-Agent Search Using Centroidal Voronoi Configuration

This paper addresses the problem of automated multiagent search in an unknown environment. Autonomous agents equipped with sensors carry out a search operation in a search space, where the uncertainty, or lack of information about the environment, is known a priori as an uncertainty density distribution function. The agents are deployed in the search space to maximize single step search effectiveness. The centroidal Voronoi configuration, which achieves a locally optimal deployment, forms the basis for the proposed sequential deploy and search strategy. It is shown that with the proposed control law the agent trajectories converge in a globally asymptotic manner to the centroidal Voronoi configuration. Simulation experiments are provided to validate the strategy. Note to Practitioners-In this paper, searching an unknown region to gather information about it is modeled as a problem of using search as a means of reducing information uncertainty about the region. Moreover, multiple automated searchers or agents are used to carry out this operation optimally. This problem has many applications in search and surveillance operations using several autonomous UAVs or mobile robots. The concept of agents converging to the centroid of their Voronoi cells, weighted with the uncertainty density, is used to design a search strategy named as sequential deploy and search. Finally, the performance of the strategy is validated using simulations.

Synthesis of epoxy-terminated polymers by radical polymerization using alpha-(t-butylperoxymethyl)styrene as chain transfer agent

Epoxy-terminated polystyrene has been synthesized by radical polymerization using alpha-(t-butylperoxymethyl) styrene (TPMS) as the chain transfer agent. The chain transfer constants were found to be 0.66 and 0.80 at 60 and 70 degrees C, respectively. The presence of epoxy end groups was confirmed by functional group modification of epoxide to aldehyde by treatment with BF3.Et(2)O. Thermal stability of TPMS was followed by differential scanning calorimetry and iodimetry. Thermal decomposition of TPMS in toluene follows first order kinetics with an activation energy of 23 kcal/mol. (C) 1996 John Wiley & Sons, Inc.

Towards building an intelligent decision support system for project management

Management of large projects, especially the ones in which a major component of R&D is involved and those requiring knowledge from diverse specialised and sophisticated fields, may be classified as semi-structured problems. In these problems, there is some knowledge about the nature of the work involved, but there are also uncertainties associated with emerging technologies. In order to draw up a plan and schedule of activities of such a large and complex project, the project manager is faced with a host of complex decisions that he has to take, such as, when to start an activity, for how long the activity is likely to continue, etc. An Intelligent Decision Support System (IDSS) which aids the manager in decision making and drawing up a feasible schedule of activities while taking into consideration the constraints of resources and time, will have a considerable impact on the efficient management of the project. This report discusses the design of an IDSS that helps in project planning phase through the scheduling phase. The IDSS uses a new project scheduling tool, the Project Influence Graph (PIG).

ICADA: Intelligent computer aided defect analysis for castings

An intelligent computer aided defect analysis (ICADA) system, based on artificial intelligence techniques, has been developed to identify design, process or material parameters which could be responsible for the occurrence of defective castings in a manufacturing campaign. The data on defective castings for a particular time frame, which is an input to the ICADA system, has been analysed. It was observed that a large proportion, i.e. 50-80% of all the defective castings produced in a foundry, have two, three or four types of defects occurring above a threshold proportion, say 10%. Also, a large number of defect types are either not found at all or found in a very small proportion, with a threshold value below 2%. An important feature of the ICADA system is the recognition of this pattern in the analysis. Thirty casting defect types and a large number of causes numbering between 50 and 70 for each, as identified in the AFS analysis of casting defects-the standard reference source for a casting process-constituted the foundation for building the knowledge base. Scientific rationale underlying the formation of a defect during the casting process was identified and 38 metacauses were coded. Process, material and design parameters which contribute to the metacauses were systematically examined and 112 were identified as rootcauses. The interconnections between defects, metacauses and rootcauses were represented as a three tier structured graph and the handling of uncertainty in the occurrence of events such as defects, metacauses and rootcauses was achieved by Bayesian analysis. The hill climbing search technique, associated with forward reasoning, was employed to recognize one or several root causes.

Fuzzy logic intelligent system for gas turbine module and system fault isolation

A fuzzy logic intelligent system is developed for gas-turbine fault isolation. The gas path measurements used for fault isolation are exhaust gas temperature, low and high rotor speed, and fuel flow. These four measurements are also called the cockpit parameters and are typically found in almost all older and newer jet engines. The fuzzy logic system uses rules developed from a model of performance influence coefficients to isolate engine faults while accounting for uncertainty in gas path measurements. It automates the reasoning process of an experienced powerplant engineer. Tests with simulated data show that the fuzzy system isolates faults with an accuracy of 89% with only the four cockpit measurements. However, if additional pressure and temperature probes between the compressors and before the burner, which are often found in newer jet engines, are considered, the fault isolation accuracy rises to as high as 98%. In addition, the additional sensors are useful in keeping the fault isolation system robust as quality of the measured data deteriorates.

Propargyloxycarbonyl (Poc) amino acid chlorides as efficient coupling reagents for the synthesis of 100% diastereopure peptides and resin bound tetrathiomolybdate as an effective deblocking agent for the Poc group

Synthesis of short peptides using propargyloxycarbonyl amino acid chlorides as effective coupling reagents and polymer supported tetrathiomolybdate as an efficient deblocking agent are reported.

Swam Intelligent Based Inverse Model for Laboratory Double Reservoir Diffusion Experiments

Theoretical approaches are of fundamental importance to predict the potential impact of waste disposal facilities on ground water contamination. Appropriate design parameters are, in general, estimated by fitting the theoretical models to a field monitoring or laboratory experimental data. Double-reservoir diffusion (Transient Through-Diffusion) experiments are generally conducted in the laboratory to estimate the mass transport parameters of the proposed barrier material. These design parameters are estimated by manual parameter adjusting techniques (also called eye-fitting) like Pollute. In this work an automated inverse model is developed to estimate the mass transport parameters from transient through-diffusion experimental data. The proposed inverse model uses particle swarm optimization (PSO) algorithm which is based on the social behaviour of animals for finding their food sources. Finite difference numerical solution of the transient through-diffusion mathematical model is integrated with the PSO algorithm to solve the inverse problem of parameter estimation.The working principle of the new solver is demonstrated by estimating mass transport parameters from the published transient through-diffusion experimental data. The estimated values are compared with the values obtained by existing procedure. The present technique is robust and efficient. The mass transport parameters are obtained with a very good precision in less time

Multi-agent search using?sensors with heterogeneous capabilities

In this thesis we address the problem of multi-agent search. We formulate two deploy and search strategies based on optimal deployment of agents in search space so as to maximize the search effectiveness in a single step. We show that a variation of centroidal Voronoi configuration is the optimal deployment. When the agents have sensors with different capabilities, the problem will be heterogeneous in nature. We introduce a new concept namely, generalized Voronoi partition in order to formulate and solve the heterogeneous multi-agent search problem. We address a few theoretical issues such as optimality of deployment, convergence and spatial distributedness of the control law and the search strategies. Simulation experiments are carried out to compare performances of the proposed strategies with a few simple search strategies.

An intelligent procurement marketplace for web services composition

This paper presents an intelligent procurement marketplace for finding the best mix of web services to dynamically compose the business process desired by a web service requester. We develop a combinatorial auction approach that leads to an integer programming formulation for the web services composition problem. The model takes into account the Quality of Service (QoS) and Service Level Agreements (SLA) for differentiating among multiple service providers who are capable of fulfilling a functionality. An important feature of the model is interface aware composition.

Multi-agent Search using Voronoi partitions

This paper addresses the problem of multiagent search in an unknown environment. The agents are autonomous in nature and are equipped with necessary sensors to carry out the search operation. The uncertainty, or lack of information about the search area is known a priori as a probability density function. The agents are deployed in an optimal way so as to maximize the one step uncertainty reduction. The agents continue to deploy themselves and reduce uncertainty till the uncertainty density is reduced over the search space below a minimum acceptable level. It has been shown, using LaSalle’s invariance principle, that a distributed control law which moves each of the agents towards the centroid of its Voronoi partition, modified by the sensor range leads to single step optimal deployment. This principle is now used to devise search trajectories for the agents. The simulations were carried out in 2D space with saturation on speeds of the agents. The results show that the control strategy per step indeed moves the agents to the respective centroid and the algorithm reduces the uncertainty distribution to the required level within a few steps.