Enlarge cover. Error rating book. Refresh and try again. Open Preview See a Problem? Details if other :. Thanks for telling us about the problem. Return to Book Page. Nir Shvalb. Irad Ben-Gal. Offers a theoretical and practical guide to the communication and navigation of autonomous mobile robots and multi-robot systems This book covers the methods and algorithms for the navigation, motion planning, and control of mobile robots acting individually and in groups.

It addresses methods of positioning in global and local coordinates systems, off-line and on-line path Offers a theoretical and practical guide to the communication and navigation of autonomous mobile robots and multi-robot systems This book covers the methods and algorithms for the navigation, motion planning, and control of mobile robots acting individually and in groups. It addresses methods of positioning in global and local coordinates systems, off-line and on-line path-planning, sensing and sensors fusion, algorithms of obstacle avoidance, swarming techniques and cooperative behavior.

The book includes ready-to-use algorithms, numerical examples and simulations, which can be directly implemented in both simple and advanced mobile robots, and is accompanied by a website hosting codes, videos, and PowerPoint slides Autonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming consists of four main parts.

The first looks at the models and algorithms of navigation and motion planning in global coordinates systems with complete information about the robot's location and velocity. The second part considers the motion of the robots in the potential field, which is defined by the environmental states of the robot's expectations and knowledge.

The robot's motion in the unknown environments and the corresponding tasks of environment mapping using sensed information is covered in the third part. The fourth part deals with the multi-robot systems and swarm dynamics in two and three dimensions. Provides a self-contained, theoretical guide to understanding mobile robot control and navigation Features implementable algorithms, numerical examples, and simulations Includes coverage of models of motion in global and local coordinates systems with and without direct communication between the robots Supplemented by a companion website offering codes, videos, and PowerPoint slides Autonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming is an excellent tool for researchers, lecturers, senior undergraduate and graduate students, and engineers dealing with mobile robots and related issues.

Get A Copy. Hardcover , pages. More Details Other Editions 2. Kalos and Whitlock [51] point out that such distinctions are not always easy to maintain. For example, the emission of radiation from atoms is a natural stochastic process. It can be simulated directly, or its average behavior can be described by stochastic equations that can themselves be solved using Monte Carlo methods. The main idea behind this method is that the results are computed based on repeated random sampling and statistical analysis. The Monte Carlo simulation is, in fact, random experimentations, in the case that, the results of these experiments are not well known.

Monte Carlo simulations are typically characterized by many unknown parameters, many of which are difficult to obtain experimentally. The only quality usually necessary to make good simulations is for the pseudo-random sequence to appear "random enough" in a certain sense. What this means depends on the application, but typically they should pass a series of statistical tests.

Testing that the numbers are uniformly distributed or follow another desired distribution when a large enough number of elements of the sequence are considered is one of the simplest and most common ones. Sawilowsky lists the characteristics of a high-quality Monte Carlo simulation: [50]. Pseudo-random number sampling algorithms are used to transform uniformly distributed pseudo-random numbers into numbers that are distributed according to a given probability distribution.

Low-discrepancy sequences are often used instead of random sampling from a space as they ensure even coverage and normally have a faster order of convergence than Monte Carlo simulations using random or pseudorandom sequences. Methods based on their use are called quasi-Monte Carlo methods. In an effort to assess the impact of random number quality on Monte Carlo simulation outcomes, astrophysical researchers tested cryptographically-secure pseudorandom numbers generated via Intel's RdRand instruction set, as compared to those derived from algorithms, like the Mersenne Twister , in Monte Carlo simulations of radio flares from brown dwarfs.

RdRand is the closest pseudorandom number generator to a true random number generator. No statistically significant difference was found between models generated with typical pseudorandom number generators and RdRand for trials consisting of the generation of 10 7 random numbers. There are ways of using probabilities that are definitely not Monte Carlo simulations — for example, deterministic modeling using single-point estimates.

Each uncertain variable within a model is assigned a "best guess" estimate. Scenarios such as best, worst, or most likely case for each input variable are chosen and the results recorded. By contrast, Monte Carlo simulations sample from a probability distribution for each variable to produce hundreds or thousands of possible outcomes. The results are analyzed to get probabilities of different outcomes occurring. The samples in such regions are called "rare events". Monte Carlo methods are especially useful for simulating phenomena with significant uncertainty in inputs and systems with many coupled degrees of freedom.

Areas of application include:. Monte Carlo methods are very important in computational physics , physical chemistry , and related applied fields, and have diverse applications from complicated quantum chromodynamics calculations to designing heat shields and aerodynamic forms as well as in modeling radiation transport for radiation dosimetry calculations.

In astrophysics , they are used in such diverse manners as to model both galaxy evolution [62] and microwave radiation transmission through a rough planetary surface. Monte Carlo methods are widely used in engineering for sensitivity analysis and quantitative probabilistic analysis in process design. The need arises from the interactive, co-linear and non-linear behavior of typical process simulations. For example,. The Intergovernmental Panel on Climate Change relies on Monte Carlo methods in probability density function analysis of radiative forcing.

The PDFs are generated based on uncertainties provided in Table 8. The combination of the individual RF agents to derive total forcing over the Industrial Era are done by Monte Carlo simulations and based on the method in Boucher and Haywood PDF of the ERF from surface albedo changes and combined contrails and contrail-induced cirrus are included in the total anthropogenic forcing, but not shown as a separate PDF.

We currently do not have ERF estimates for some forcing mechanisms: ozone, land use, solar, etc. Monte Carlo methods are used in various fields of computational biology , for example for Bayesian inference in phylogeny , or for studying biological systems such as genomes, proteins, [73] or membranes. Computer simulations allow us to monitor the local environment of a particular molecule to see if some chemical reaction is happening for instance.

Path tracing , occasionally referred to as Monte Carlo ray tracing, renders a 3D scene by randomly tracing samples of possible light paths. Repeated sampling of any given pixel will eventually cause the average of the samples to converge on the correct solution of the rendering equation , making it one of the most physically accurate 3D graphics rendering methods in existence. The standards for Monte Carlo experiments in statistics were set by Sawilowsky. Monte Carlo methods are also a compromise between approximate randomization and permutation tests. An approximate randomization test is based on a specified subset of all permutations which entails potentially enormous housekeeping of which permutations have been considered.

The Monte Carlo approach is based on a specified number of randomly drawn permutations exchanging a minor loss in precision if a permutation is drawn twice—or more frequently—for the efficiency of not having to track which permutations have already been selected. Monte Carlo methods have been developed into a technique called Monte-Carlo tree search that is useful for searching for the best move in a game. Possible moves are organized in a search tree and many random simulations are used to estimate the long-term potential of each move.

A black box simulator represents the opponent's moves. The net effect, over the course of many simulated games, is that the value of a node representing a move will go up or down, hopefully corresponding to whether or not that node represents a good move. Monte Carlo methods are also efficient in solving coupled integral differential equations of radiation fields and energy transport, and thus these methods have been used in global illumination computations that produce photo-realistic images of virtual 3D models, with applications in video games , architecture , design , computer generated films , and cinematic special effects.

Each simulation can generate as many as ten thousand data points that are randomly distributed based upon provided variables. Ultimately this serves as a practical application of probability distribution in order to provide the swiftest and most expedient method of rescue, saving both lives and resources. Monte Carlo simulation is commonly used to evaluate the risk and uncertainty that would affect the outcome of different decision options.

Monte Carlo simulation allows the business risk analyst to incorporate the total effects of uncertainty in variables like sales volume, commodity and labour prices, interest and exchange rates, as well as the effect of distinct risk events like the cancellation of a contract or the change of a tax law.

Monte Carlo methods in finance are often used to evaluate investments in projects at a business unit or corporate level, or to evaluate financial derivatives. They can be used to model project schedules , where simulations aggregate estimates for worst-case, best-case, and most likely durations for each task to determine outcomes for the overall project. A Monte Carlo approach was used for evaluating the potential value of a proposed program to help female petitioners in Wisconsin be successful in their applications for harassment and domestic abuse restraining orders.

It was proposed to help women succeed in their petitions by providing them with greater advocacy thereby potentially reducing the risk of rape and physical assault. However, there were many variables in play that could not be estimated perfectly, including the effectiveness of restraining orders, the success rate of petitioners both with and without advocacy, and many others. The study ran trials that varied these variables to come up with an overall estimate of the success level of the proposed program as a whole. In general, the Monte Carlo methods are used in mathematics to solve various problems by generating suitable random numbers see also Random number generation and observing that fraction of the numbers that obeys some property or properties.

The method is useful for obtaining numerical solutions to problems too complicated to solve analytically. The most common application of the Monte Carlo method is Monte Carlo integration. Deterministic numerical integration algorithms work well in a small number of dimensions, but encounter two problems when the functions have many variables.

First, the number of function evaluations needed increases rapidly with the number of dimensions. For example, if 10 evaluations provide adequate accuracy in one dimension, then 10 points are needed for dimensions—far too many to be computed. This is called the curse of dimensionality. Second, the boundary of a multidimensional region may be very complicated, so it may not be feasible to reduce the problem to an iterated integral.

Monte Carlo methods provide a way out of this exponential increase in computation time. As long as the function in question is reasonably well-behaved , it can be estimated by randomly selecting points in dimensional space, and taking some kind of average of the function values at these points. A refinement of this method, known as importance sampling in statistics, involves sampling the points randomly, but more frequently where the integrand is large.

To do this precisely one would have to already know the integral, but one can approximate the integral by an integral of a similar function or use adaptive routines such as stratified sampling , recursive stratified sampling , adaptive umbrella sampling [96] [97] or the VEGAS algorithm. A similar approach, the quasi-Monte Carlo method , uses low-discrepancy sequences. These sequences "fill" the area better and sample the most important points more frequently, so quasi-Monte Carlo methods can often converge on the integral more quickly.

### Introduction

Another class of methods for sampling points in a volume is to simulate random walks over it Markov chain Monte Carlo. Another powerful and very popular application for random numbers in numerical simulation is in numerical optimization. The problem is to minimize or maximize functions of some vector that often has many dimensions.

Many problems can be phrased in this way: for example, a computer chess program could be seen as trying to find the set of, say, 10 moves that produces the best evaluation function at the end. In the traveling salesman problem the goal is to minimize distance traveled.

There are also applications to engineering design, such as multidisciplinary design optimization. It has been applied with quasi-one-dimensional models to solve particle dynamics problems by efficiently exploring large configuration space. Reference [99] is a comprehensive review of many issues related to simulation and optimization.

The traveling salesman problem is what is called a conventional optimization problem. That is, all the facts distances between each destination point needed to determine the optimal path to follow are known with certainty and the goal is to run through the possible travel choices to come up with the one with the lowest total distance. However, let's assume that instead of wanting to minimize the total distance traveled to visit each desired destination, we wanted to minimize the total time needed to reach each destination.

This goes beyond conventional optimization since travel time is inherently uncertain traffic jams, time of day, etc. As a result, to determine our optimal path we would want to use simulation - optimization to first understand the range of potential times it could take to go from one point to another represented by a probability distribution in this case rather than a specific distance and then optimize our travel decisions to identify the best path to follow taking that uncertainty into account.

Probabilistic formulation of inverse problems leads to the definition of a probability distribution in the model space.

## Book Probabilistic Search For Tracking Targets Theory And Modern Applications

This probability distribution combines prior information with new information obtained by measuring some observable parameters data. As, in the general case, the theory linking data with model parameters is nonlinear, the posterior probability in the model space may not be easy to describe it may be multimodal, some moments may not be defined, etc. When analyzing an inverse problem, obtaining a maximum likelihood model is usually not sufficient, as we normally also wish to have information on the resolution power of the data.

In the general case we may have many model parameters, and an inspection of the marginal probability densities of interest may be impractical, or even useless.

But it is possible to pseudorandomly generate a large collection of models according to the posterior probability distribution and to analyze and display the models in such a way that information on the relative likelihoods of model properties is conveyed to the spectator. This can be accomplished by means of an efficient Monte Carlo method, even in cases where no explicit formula for the a priori distribution is available.

The best-known importance sampling method, the Metropolis algorithm, can be generalized, and this gives a method that allows analysis of possibly highly nonlinear inverse problems with complex a priori information and data with an arbitrary noise distribution. From Wikipedia, the free encyclopedia. Not to be confused with Monte Carlo algorithm. Probabilistic problem-solving algorithm. Fluid dynamics.

Monte Carlo methods. See also: Monte Carlo method in statistical physics. Main article: Monte Carlo tree search. See also: Computer Go. See also: Monte Carlo methods in finance , Quasi-Monte Carlo methods in finance , Monte Carlo methods for option pricing , Stochastic modelling insurance , and Stochastic asset model. Main article: Monte Carlo integration. Main article: Stochastic optimization. Statistics portal. October The Journal of Chemical Physics.

Bibcode : JChPh.. Bibcode : Bimka.. Journal of the American Statistical Association. Computational Statistics. Nonlinear Markov processes. Cambridge Univ. Mean field simulation for Monte Carlo integration. Xiphias Press. Retrieved Bibcode : PNAS LIX : — Methodos : 45— Methodos : — Feynman—Kac formulae. Genealogical and interacting particle approximations. Probability and Its Applications. Lecture Notes in Mathematics. Berlin: Springer.

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## Probabilistic Search for Tracking Targets : Theory and Modern Applications

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