Author

# Robert D. Braun

Other affiliations: Stanford University, Georgia Institute of Technology, Langley Research Center

Bio: Robert D. Braun is an academic researcher from University of Colorado Boulder. The author has contributed to research in topics: Mars Exploration Program & Exploration of Mars. The author has an hindex of 36, co-authored 278 publications receiving 5801 citations. Previous affiliations of Robert D. Braun include Stanford University & Georgia Institute of Technology.

##### Papers published on a yearly basis

##### Papers

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TL;DR: The United States has successfully landed five robotic systems on the surface of Mars as mentioned in this paper, all of which had landing mass below 0.6 metric tons (t), had landing footprints on the order of hundreds of km and landing at sites below -1 km MOLA elevation due to the need to perform entry, descent and landing operations in an environment with sufficient atmospheric density.

Abstract: The United States has successfully landed five robotic systems on the surface of Mars. These systems all had landed mass below 0.6 metric tons (t), had landed footprints on the order of hundreds of km and landed at sites below -1 km MOLA elevation due the need to perform entry, descent and landing operations in an environment with sufficient atmospheric density. Current plans for human exploration of Mars call for the landing of 40-80 t surface elements at scientifically interesting locations within close proximity (10's of m) of pre-positioned robotic assets. This paper summarizes past successful entry, descent and landing systems and approaches being developed by the robotic Mars exploration program to increased landed performance (mass, accuracy and surface elevation). In addition, the entry, descent and landing sequence for a human exploration system will be reviewed, highlighting the technology and systems advances required.

495 citations

03 Oct 1996

TL;DR: The fundamental concepts leading to the development of the collaborative architecture are presented and the architecture's mathematical foundation is discussed and its characteristics are shown to be best suited for large-scale, highly-constrained, distributed-analysis applications.

Abstract: Collaborative optimization is a design architecture specifically created for large-scale distributed-analysis applications. In this approach, a problem is decomposed along domain-specific boundaries into a user-defined number of subspaces which are driven towards interdisciplinary compatibility and the appropriate solution by a system-level coordination process. This design approach allows domain-specific issues to be accommodated by disciplinary analysts, while requiring interdisciplinary decisions to be reached by consensus. In a large-scale practical design environment, this scheme has several advantages over traditional solution strategies. These advantageous features include reducing the amount of information transferred between disciplines, the removal of large iteration-loops, allowing the use of customized optimizers within the domain-specific subspace analyses, a solution framework that is easily parallelized and operable on heterogeneous equipment, and a structural framework that is well-suited for conventional disciplinary organizations. In this dissertation, the fundamental concepts leading to the development of the collaborative architecture are presented and the architecture's mathematical foundation is discussed. The design architecture is shown to be applicable to any set of arbitrarily-connected analyses, regardless of the interdisciplinary coupling structure. Example applications in trajectory optimization and launch vehicle design illustrate the the architecture's potential for use in large-scale design applications. Applied in a multidisciplinary design environment, numerous operational advantages of this optimization scheme are demonstrated. These advantageous features are a direct result of empowering the subspaces in the domain-specific decision process, thereby distributing design authority as well as analysis responsibility. While applicable to any problem, the characteristics of the collaborative optimization architecture are shown to be best suited for large-scale, highly-constrained, distributed-analysis applications. Because such problems are common in practical design settings, the collaborative optimization architecture should provide design teams with an intriguing alternative to current practices.

310 citations

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04 Mar 2006TL;DR: The United States has successfully landed five robotic systems on the surface of Mars as discussed by the authors, all of which had landing mass below 0.6 metric tons (t), had landing footprints on the order of hundreds of km and landing at sites below -1 km MOLA elevation due to the need to perform entry, descent and landing operations in an environment with sufficient atmospheric density.

Abstract: The United States has successfully landed five robotic systems on the surface of Mars. These systems all had landed mass below 0.6 metric tons (t), had landed footprints on the order of hundreds of km and landed at sites below -1 km MOLA elevation due the need to perform entry, descent and landing operations in an environment with sufficient atmospheric density. Current plans for human exploration of Mars call for the landing of 40-80 t surface elements at scientifically interesting locations within close proximity (10's of m) of pre-positioned robotic assets. This paper summarizes past successful entry, descent and landing systems and approaches being developed by the robotic Mars exploration program to increased landed performance (mass, accuracy and surface elevation). In addition, the entry, descent and landing sequence for a human exploration system will be reviewed, highlighting the technology and systems advances required.

282 citations

01 Aug 1995

TL;DR: The collaborative architecture is developed and its mathematical foundation is presented and an example application is presented which highlights the potential of this method for use in large-scale design applications.

Abstract: Collaborative optimization is a design architecture applicable in any multidisciplinary analysis environment but specifically intended for large-scale distributed analysis applications. In this approach, a complex problem is hierarchically decomposed along disciplinary boundaries into a number of subproblems which are brought into multidisciplinary agreement by a system-level coordination process. When applied to problems in a multidisciplinary design environment, this scheme has several advantages over traditional solution strategies. These advantageous features include reducing the amount of information transferred between disciplines, the removal of large iteration-loops, allowing the use of different subspace optimizers among the various analysis groups, an analysis framework which is easily parallelized and can operate on heterogenous equipment, and a structural framework that is well-suited for conventional disciplinary organizations. In this article, the collaborative architecture is developed and its mathematical foundation is presented. An example application is also presented which highlights the potential of this method for use in large-scale design applications.

258 citations

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01 Sep 1996TL;DR: The present investigation focuses on application of the collaborative optimization architecutre to the multidisciplinary design of a single-stage-to-orbit launch vehicle, demonstrating the difference between minimum weight and minimum cost concepts.

Abstract: Collaborative optimization is a new design architecture specifically created for large-scale distributed-analysis applications. In this approach, a problem is decomposed into a user-defined number of subspace optimization problems that are driven towards interdisciplinary compatibility and the appropriate solution by a system-level coordination process. This decentralized design strategy allows domain-specific issues to be accommodated by disciplinary analysts, while requiring interdisciplinary decisions to be reached by consensus. The present investigation focuses on application of the collaborative optimization architecutre to the multidisciplinary design of a single-stage-to-orbit launch vehicle. Vehicle design, trajectory, and cost issues are directly modeled. Posed to suit the collaborative architecture, the design problem is characterized by 95 design variables and 16 constraints. Numerous collaborative solutions are obtained. Comparison of these solutions demonstrates the influence which an a priori ascent-abort criterion has on development cost. Similarly, objective-function selection is discussed, demonstrating the difference between minimum weight and minimum cost concepts. The operational advantages of the collaborative optimization architecutre in a multidisciplinary design environment are also discussed.

236 citations

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TL;DR: This book by a teacher of statistics (as well as a consultant for "experimenters") is a comprehensive study of the philosophical background for the statistical design of experiment.

Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

13,333 citations

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TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Microsoft

^{1}TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

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TL;DR: A number of chemical-kinetic problems related to phenomena occurring behind a shock wave surrounding an object flying in the earth atmosphere are discussed in this paper, including the nonequilibrium thermochemical relaxation phenomena behind a wave surrounding the flying object.

Abstract: A number of chemical-kinetic problems related to phenomena occurring behind a shock wave surrounding an object flying in the earth atmosphere are discussed, including the nonequilibrium thermochemical relaxation phenomena occurring behind a shock wave surrounding the flying object, problems related to aerobraking maneuver, the radiation phenomena for shock velocities of up to 12 km/sec, and the determination of rate coefficients for ionization reactions and associated electron-impact ionization reactions. Results of experiments are presented in form of graphs and tables, giving data on the reaction rate coefficients for air, the ionization distances, thermodynamic properties behind a shock wave, radiative heat flux calculations, Damkoehler numbers for the ablation-product layer, together with conclusions.

1,287 citations