Factor Graphs for Heterogeneous Bayesian Decentralized Data Fusion

This paper explores the use of factor graphs as an inference and analysis tool for Bayesian peer-to-peer decentralized data fusion. We propose a framework by which agents can each use local factor graphs to represent relevant partitions of a complex global joint probability distribution, thus allowing them to avoid reasoning over the entirety of a more complex model and saving communication as well as computation cost. This allows heterogeneous multi-robot systems to cooperate on a variety of real world, task oriented missions, where scalability and modularity are key. To develop the initial theory and analyze the limits of this approach, we focus our attention on static linear Gaussian systems in tree-structured networks and use Channel Filters (also represented by factor graphs) to explicitly track common information. We discuss how this representation can be used to describe various multi-robot applications and to design and analyze new heterogeneous data fusion algorithms. We validate our method in simulations of a multi-agent multi-target tracking and cooperative multi-agent mapping problems, and discuss the computation and communication gains of this approach.Comment: 8 pages, 6 figures, 1 table, submitted to the 24th International Conference on Information Fusio

Marichev-Saigo-Maeda Fractional Integration Operators Involving Generalized Bessel Functions

Two integral operators involving Appell's functions, or Horn's function in the kernel are considered. Composition of such functions with generalized Bessel functions of the first kind is expressed in terms of generalized Wright function and generalized hypergeometric series. Many special cases, including cosine and sine function, are also discussed

A Review on Preparation of ZnO Nanorods and Their Use in Ethanol Vapors Sensing

The devices of polycrystalline film have small sensitivity that can be overthrown by using high aspect ratio of 1D nanostructures, such as ZnO nanostructures. Sensors based on 1D nanostructures show very quick response time and high sensitivity for their high impact factor. The purpose of this article is to provide a comparison of different methods and the quality of the sensors thus produced. Currently, metal oxide 1D nanoarchitectures like ZnO have great attraction due to their applications in sensors. Metal oxide nanostructures have high aspect ratio, with small consumption of power and low weight, however, keeping excellent chemical and thermal dependability. Different techniques have been adopted to fabricate metal oxide one-dimensional nanostructures like hydrothermal, electro-spinning, sol-gel, ultrasonic irradiation, anodization, solid state chemical reaction, molten-salt, thermal evaporation, carbothermal reduction, aerosol, vapor-phase transport, chemical vapor deposition, RF sputtering, gas-phase-assisted nanocarving, molecular beam epitaxy, dry plasma etching, and UV lithography. The sensitivity depends upon the materials; synthesis technique and morphology of the sensor performance toward a particular gas have different range of success. This article estimates the efficiency of ZnO 1D nanoarchitectures, gas sensors. Finally, in this review, we had mentioned the future directions of investigations in this field

Heterogeneous Bayesian Decentralized Data Fusion: An Empirical Study

In multi-robot applications, inference over large state spaces can often be divided into smaller overlapping sub-problems that can then be collaboratively solved in parallel over `separate' subsets of states. To this end, the factor graph decentralized data fusion (FG-DDF) framework was developed to analyze and exploit conditional independence in heterogeneous Bayesian decentralized fusion problems, in which robots update and fuse pdfs over different locally overlapping random states. This allows robots to efficiently use smaller probabilistic models and sparse message passing to accurately and scalably fuse relevant local parts of a larger global joint state pdf, while accounting for data dependencies between robots. Whereas prior work required limiting assumptions about network connectivity and model linearity, this paper relaxes these to empirically explore the applicability and robustness of FG-DDF in more general settings. We develop a new heterogeneous fusion rule which generalizes the homogeneous covariance intersection algorithm, and test it in multi-robot tracking and localization scenarios with non-linear motion/observation models under communication dropout. Simulation and linear hardware experiments show that, in practice, the FG-DDF continues to provide consistent filtered estimates under these more practical operating conditions, while reducing computation and communication costs by more than 95%, thus enabling the design of scalable real-world multi-robot systems.Comment: 7 pages, 2 figures, submitted to IEEE Conference on Robotics and Automation (ICRA 2023

Composition Formulas of Bessel-Struve Kernel Function

The object of this paper is to study and develop the generalized fractional calculus operators involving Appell’s function F3(·) due to Marichev-Saigo-Maeda. Here, we establish the generalized fractional calculus formulas involving Bessel-Struve kernel function Sαλz,  λ,z∈C to obtain the results in terms of generalized Wright functions. The representations of Bessel-Struve kernel function in terms of exponential function and its relation with Bessel and Struve function are also discussed. The pathway integral representations of Bessel-Struve kernel function are also given in this study

Exploiting Structure for Optimal Multi-Agent Bayesian Decentralized Estimation

A key challenge in Bayesian decentralized data fusion is the `rumor propagation' or `double counting' phenomenon, where previously sent data circulates back to its sender. It is often addressed by approximate methods like covariance intersection (CI) which takes a weighted average of the estimates to compute the bound. The problem is that this bound is not tight, i.e. the estimate is often over-conservative. In this paper, we show that by exploiting the probabilistic independence structure in multi-agent decentralized fusion problems a tighter bound can be found using (i) an expansion to the CI algorithm that uses multiple (non-monolithic) weighting factors instead of one (monolithic) factor in the original CI and (ii) a general optimization scheme that is able to compute optimal bounds and fully exploit an arbitrary dependency structure. We compare our methods and show that on a simple problem, they converge to the same solution. We then test our new non-monolithic CI algorithm on a large-scale target tracking simulation and show that it achieves a tighter bound and a more accurate estimate compared to the original monolithic CI.Comment: 4 pages, 4 figures. presented at the Inference and Decision Making for Autonomous Vehicles (IDMAV) RSS 2023 worksho