WorldCat Identities

LaValle, Steven Michael 1968-

Works: 35 works in 62 publications in 2 languages and 532 library holdings
Genres: Academic theses 
Roles: Author
Classifications: TJ211.4, 629.8932
Publication Timeline
Most widely held works by Steven Michael LaValle
Planning algorithms by Steven Michael LaValle( Book )

19 editions published between 2006 and 2014 in English and held by 404 WorldCat member libraries worldwide

"Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces." "Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology."--Jacket
Sensing and filtering : a fresh perspective based on preimages and information spaces by Steven Michael LaValle( )

9 editions published between 2012 and 2014 in English and held by 49 WorldCat member libraries worldwide

This paper presents an unusual perspective on sensing uncertainty and filtering with the intention of understanding what information is minimally needed to achieve a specified task. Information itself is modeled using information space concepts, which originated from dynamic game theory (rather than information theory, which was developed mainly for communication). The guiding principle in this monograph is avoid sensing, representing, and encoding more than is necessary. The concepts and tools are motivated by many tasks of current interest, such as tracking, monitoring, navigation, pursuit-evasion, exploration, and mapping. First, an overview of sensors that appear in numerous systems is presented. Following this, the notion of a virtual sensor is explained, which provides a mathematical way to model numerous sensors while abstracting away their particular physical implementation. Dozens of useful models are given, each as a mapping from the physical world to the set of possible sensor outputs. Preimages with respect to this mapping represent a fundamental source of uncertainty: These are equivalence classes of physical states that would produce the same sensor output. Pursuing this idea further, the powerful notion of a sensor lattice is introduced, in which all possible virtual sensors can be rigorously compared. The next part introduces filters that aggregate information from multiple sensor readings. The integration of information over space and time is considered. In the spatial setting, classical triangulation methods are expressed in terms of preimages. In the temporal setting, an information-space framework is introduced that encompasses familiar Kalman and Bayesian filters, but also introduces a novel family called combinatorial filters. Finally, the planning problem is presented in terms of filters and information spaces. The monograph concludes with some discussion about connections to many related research fields and numerous open problems and future research directions
Minimalist multi-agent filtering and guidance by Jaime Bobadilla Molina( )

1 edition published in 2013 in English and held by 34 WorldCat member libraries worldwide

Extracting visibility information by following walls( )

1 edition published in 2007 in English and held by 15 WorldCat member libraries worldwide

A game-theoretic framework for robot motion planning by Steven Michael LaValle( )

2 editions published in 1995 in English and held by 2 WorldCat member libraries worldwide

The primary contribution of this dissertation is the presentation of a dynamic game-theoretic framework that is used as an analytical tool and unifying perspective for a wide class of problems in robot motion planning. The framework provides a precise mathematical characterization that can incorporate any of the essential features of decision theory, stochastic optimal control, and traditional multiplayer games. The determination of strategies that optimize some precise performance functionals is central to these subjects, and is of fundamental value for many types of motion planning problems. The basic motion planning problem is to compute a collision-free trajectory for the robot, given perfect sensing, an exact representation of the environment, and completely predictable execution. The best-known algorithms have exponential complexity, and most extensions to the basic problem are provably intractable. The techniques in this dissertation characterize several extensions to the basic motion planning problem, and lead to computational techniques that provide practical, approximate solutions. A general perspective on motion planning is also provided by relating the similarities between various extensions to the basic problem within a common mathematical framework. Modeling, analysis, algorithms, and computed examples are presented for each of three problems: (1) motion planning under uncertainty in sensing and control; (2) motion planning under environment uncertainties; and (3) multiple-robot motion planning. Traditional approaches to the first problem are often based on a methodology known as preimage planning, which involves worst-case analysis. In this context, a general method for determining feedback strategies is developed by blending ideas from stochastic optimal control and dynamic game theory with traditional preimage planning concepts. This generalizes classical preimages to performance preimages and preimage plans to motion strategies with information feedback. For the second problem, robot strategies are analyzed and determined for situations in which the environment of the robot is changing, but not completely predictable. Several new applications are identified for this context. The changing environment is treated in a flexible manner by combining traditional configuration space concepts with stochastic optimal control concepts. For the third problem, dynamic game-theoretic and multiobjective optimization concepts are applied to motion planning for multiple robots. This allows the synthesis of motion plans that simultaneously optimize an independent performance criterion for each robot. Several versions of the formulation are considered: fixed-path coordination, coordination on independent configuration-space roadmaps, and centralized planning
Combinatorial structures and filter design in information spaces by Jingjin Yu( )

1 edition published in 2013 in English and held by 1 WorldCat member library worldwide

Navigation in an unfamiliar environment using signal intensity by Kamilah J Taylor( )

1 edition published in 2011 in English and held by 1 WorldCat member library worldwide

This thesis introduces a sensor-based planning algorithm that uses less sensing information than any others within the family of bug algorithms. The robot is unable to access precise information regarding position coordinates, angular coordinates, time, or odometry, but is nevertheless able to navigate itself to a goal among unknown piecewise-analytic obstacles in the plane. The only sensor providing real values is an intensity sensor, which measures the signal strength emanating from the goal. The signal intensity function may or may not be symmetric; the main requirement is that the level sets are concentric images of simple closed curves, i.e. topological circles. Convergence analysis and distance bounds are established for the presented approach. The algorithm is then experimentally verified using a differential drive robot and an infrared beacon
Learning Combinatorial Map Information from Permutations of Landmarks( Book )

1 edition published in 2010 in English and held by 1 WorldCat member library worldwide

This paper considers a robot that moves in the plane and is only able to sense the cyclic order of landmarks with respect to its current position. No metric information is available regarding the robot or landmark positions; moreover, the robot does not have a compass or odometers (i.e., knowledge of coordinates). We carefully study the information space of the robot, and establish its capabilities in terms of mapping the environment and accomplishing tasks, such as navigation and patrolling. When the robot moves exclusively inside the convex hull of the set of landmarks, the information space can be succinctly characterized as an order type, which provides information powerful enough to determine which points lie inside the convex hulls of subsets of landmarks. Additionally, if the robot is allowed to move outside the convex hull of the set of landmarks, the information space is described with a swap cell decomposition, which is an aspect graph in which each aspect is a cyclic permutation of landmarks. We show how to construct such decomposition through its dual, using two kinds of feedback motion commands based on the landmarks sensed
A high performance vector rendering pipeline by Apollo Isaac Orion Ellis( )

1 edition published in 2018 in English and held by 1 WorldCat member library worldwide

Vector images are images which encode visible surfaces of a 3D scene, in a resolution independent format. Prior to this work generation of such an image was not real time. As such the benefits of using them in the graphics pipeline were not fully expressed.In this thesis we propose methods for addressing the following questions. How can we introduce vector images into the graphics pipeline, namingly, how can we produce them in real time. How can we take advantage of resolution independence, and how can we render vector images to a pixel display as efficiently as possible and with the highest quality.There are three main contributions of this work. We have designed a real time vector rendering system. That is, we present a GPU accelerated pipeline which takes as an input a scene with 3D geometry, and outputs a vector image. We call this system SVGPU: Scalable Vector Graphics on the GPU.As mentioned vector images are resolution independent. We have designed a cloud pipeline for streaming vector images. That is, we present system design and optimizations for streaming vector images across interconnection networks, which reduces the bandwidth required for transporting real time 3D content from server to client.Lastly, in this thesis we introduce another added benefit of vector images. We have created a method for rendering them with the highest possible quality. That is, we have designed a new set of operations on vector images, which allows us to anti-alias them during rendering to a canonical 2D image.Our contributions provide the system design, optimizations, and algorithms required to bring vector image utilization and benefits much closer to the real time graphics pipeline. Together they form an end to end pipeline to this purpose, i.e. "A High Performance Vector Rendering Pipeline."
Fast numerical algorithms for optimal robot motion planning by Dmytro Yershov( )

1 edition published in 2013 in English and held by 1 WorldCat member library worldwide

A Bayesian framework for considering probability distributions of image segments and segmentations by Steven Michael LaValle( )

1 edition published in 1993 in English and held by 1 WorldCat member library worldwide

Human control of robots over discrete noisy channels with high latency: toward efficient EEG-based brain-robot interfaces by Abdullah Akce( )

1 edition published in 2013 in English and held by 1 WorldCat member library worldwide

Optimizing robot motion strategies for assembly with stochastic models of the assembly process by Rajeev Sharma( )

1 edition published in 1996 in English and held by 1 WorldCat member library worldwide

Reasoning and decisions in partially observable games by Mark Richards( )

1 edition published in 2012 in English and held by 1 WorldCat member library worldwide

Algorithms for computing numerical optimal feedback motion strategies by Steven Michael LaValle( )

1 edition published in 2001 in English and held by 1 WorldCat member library worldwide

Gui hua suan fa by Steven Michael LaValle( Book )

1 edition published in 2011 in Chinese and held by 1 WorldCat member library worldwide

Minimalist models and methods for visibility-based tasks by Benjamin Tovar( )

1 edition published in 2009 in English and held by 1 WorldCat member library worldwide

This dissertation proposes minimal models for solving visibility-based robotic tasks. It introduces strategies that handle sensing and actuation uncertainty while avoiding precise state estimations. This is done by analyzing the space of sensing and actuation histories, the history information space. The history information space is compressed into smaller spaces, called derived information spaces, which are used for filtering and planning. By designing and analyzing the derived information spaces, we determine minimal information requirements to solve the robotic tasks. In this context, minimal information refers to the detection combinatorial properties of the environment necessary to complete the task. Examples of these combinatorial properties are the order type of a configuration of landmarks, or the inflection arrangement of a polygonal boundary. By establishing that certain tasks can be solved using simple sensors that detect these properties, formal performance guarantees are made while avoiding substantial modeling challenges. From this perspective, the thesis provides novel strategies for classical robotic tasks, such as navigation in unknown planar environments, navigation among unknown sets of landmarks, and visibility-based pursuit-evasion. Information is recovered from combinatorial events with models of sensors unable to gather metric information (e.g., distances or angles), or global reference frames (e.g., without a compass, or a global positioning system). These combinatorial events served as the base of a sensor beam abstraction, from which several inferences about the path followed by the robot are made
Minimalist Hardware Architectures for Agent Tracking and Guidance by Justin T Czarnowski( )

1 edition published in 2011 in English and held by 1 WorldCat member library worldwide

The philosophy of minimalism in robotics promotes gaining an understanding of sensing and computational requirements for solving a task. This minimalist approach lies in contrast to the common practice of first taking an existing sensory motor system, and only afterwards determining how to apply the robotic system to the task. While it may seem convenient to simply apply existing hardware systems to the task at hand, this design philosophy often proves to be wasteful in terms of energy consumption and cost, along with unnecessary complexity and decreased reliability. While impressive in terms of their versatility, complex robots such as the PR2 (which cost hundreds of thousands of dollars) are impractical for many common applications. Instead, if a specific task is required, sensing and computational requirements can be determined specific to that task, and a clever hardware implementation can be built to accomplish the task. Since this minimalist hardware would be designed around accomplishing the specified task, significant reductions in hardware complexity can be obtained. This can lead to huge advantages in battery life, cost, and reliability. Even if cost is of no concern, battery life is often a limiting factor in many applications. Thus, a minimalist hardware system is critical in achieving the system requirements. In this thesis, we will discuss an implementation of a counting, tracking, and actuation system as it relates to ergodic bodies to illustrate a minimalist design methodology
Lifted Inference for Relational Hybrid Models by Jaesik Choi( )

1 edition published in 2012 in English and held by 1 WorldCat member library worldwide

Rendezvous of multiple Dubins car agents with minimal sensing and control requirements by Jingjin Yu( )

1 edition published in 2010 in English and held by 1 WorldCat member library worldwide

We study minimalism in sensing and control by considering a multi-agent system in which each agent moves like a Dubins car and has a limited sensor that reports only the presence of another agent within some sector of its windshield. Using a very simple quantized control law with three values, each agent tracks another agent assigned to it by maintaining that agent within this windshield sector. We use Lyapunov analysis to show that by acting autonomously in this way, the agents will achieve rendezvous given a connected initial assignment graph and a merge assumption. We then proceed to show that, with a slightly different control law, an initial assignment is not required and the sensing model can be weakened further. A distinguishing feature of our approach is that it does not involve any estimation procedure aimed at reconstructing coordinate information. Our scenario thus appears to be the first example in which an interesting task is performed with extremely coarse sensing and control, and without state estimation. The system was implemented in computer simulation, accessible through the Web, of which the results are presented in the thesis
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Planning algorithms
Alternative Names
La Valle, Steven M. 1968-

LaValle, Steven M.

LaValle, Steven M. 1968-

LaValle, Steven M. (Steven Michael), 1968-

Steven M. LaValle Amerikaans informaticus

Steven M. LaValle roboticist

Valle, Steven M. la 1968-