Nancy M. Amato

MAPRM: a probabilistic roadmap planner with sampling on the medial axis of the free space

Probabilistic roadmap planning methods have been shown to perform well in a number of practical situations, but their performance degrades when paths are required to pass through narrow passages in the free space. We propose a new method of sampling the configuration space in which randomly generated configurations, free or not, are retracted onto the medial axis of the free space. We give algorithms that perform this retraction while avoiding explicit computation of the medial axis, and we show that sampling and retracting in this manner increases the number of nodes found in small volume corridors in a way that is independent of the volume of the corridor and depends only on the characteristics of the obstacles bounding it. Theoretical and experimental results are given to show that this improves performance on problems requiring traversal of narrow passages.

Choosing good distance metrics and local planners for probabilistic roadmap methods

This paper presents a comparative evaluation of different distance metrics and local planners within the context of probabilistic roadmap methods for planning the motion of rigid objects in three-dimensional workspaces. The study concentrates on cluttered three-dimensional workspaces typical of, for example, virtual prototyping applications such as maintainability studies in mechanical CAD designs. Our results include recommendations for selecting appropriate combinations of distance metrics and local planners for such applications. Our study of distance metrics shows that the importance of the translational distance increases relative to the rotational distance as the environment becomes more crowded. We find that each local planner makes some connections than none of the others do—indicating that better connected roadmaps will be constructed using multiple local planners. We propose a new local planning method we call rotate-atthat often outperforms the common straight-line in C-space method in crowded environments.

A framework for adaptive algorithm selection in STAPL

Writing portable programs that perform well on multiple platforms or for varying input sizes and types can be very difficult because performance is often sensitive to the system architecture, the run-time environment, and input data characteristics. This is even more challenging on parallel and distributed systems due to the wide variety of system architectures. One way to address this problem is to adaptively select the best parallel algorithm for the current input data and system from a set of functionally equivalent algorithmic options. Toward this goal, we have developed a general framework for adaptive algorithm selection for use in the Standard Template Adaptive Parallel Library (STAPL). Our framework uses machine learning techniques to analyze data collected by STAPL installation benchmarks and to determine tests that will select among algorithmic options at run-time. We apply a prototype implementation of our framework to two important parallel operations, sorting and matrix multiplication, on multiple platforms and show that the framework determines run-time tests that correctly select the best performing algorithm from among several competing algorithmic options in 86-100% of the cases studied, depending on the operation and the system.

STAPL: an adaptive, generic parallel C++ library

The Standard Template Adaptive Parallel Library (STAPL) is a parallel library designed as a superset of the ANSI C++ Standard Template Library (STL). It is sequentially consistent for functions with the same name, and executes on uni- or multi-processor systems that utilize shared or distributed memory. STAPL is implemented using simple parallel extensions of C++ that currently provide a SPMD model of parallelism, and supports nested parallelism. The library is intended to be general purpose, but emphasizes irregular programs to allow the exploitation of parallelism for applications which use dynamically linked data structures such as particle transport calculations, molecular dynamics, geometric modeling, and graph algorithms. STAPL provides several different algorithms for some library routines, and selects among them adaptively at runtime. STAPL can replace STL automatically by invoking a preprocessing translation phase. In the applications studied, the performance of translated code was within 5% of the results obtained using STAPL directly. STAPL also provides functionality to allow the user to further optimize the code and achieve additional performance gains. We present results obtained using STAPL for a molecular dynamics code and a particle transport code.

Simultaneous shape decomposition and skeletonization

Shape decomposition and skeletonization share many common properties and applications. However, they are generally treated as independent computations. In this paper, we propose an iterative approach that simultaneously generates a hierarchical shape decomposition and a corresponding set of multi-resolution skeletons. In our method, a skeleton of a model is extracted from the components of its decomposition --- that is, both processes and the qualities of their results are interdependent. In particular, if the quality of the extracted skeleton does not meet some user specified criteria, then the model is decomposed into finer components and a new skeleton is extracted from these components. The process of simultaneous shape decomposition and skeletonization iterates until the quality of the skeleton becomes satisfactory. We provide evidence that the proposed framework is efficient and robust under perturbation and. deformation. We also demonstrate that our results can readily be used in problems including skeletal deformations and virtual reality navigation.

Using motion planning to map protein folding landscapes and analyze folding kinetics of known native structures

We present a novel approach for studying the kinetics of protein folding. The framework has evolved from robotics motion planning techniques called probabilistic roadmap methods (prms) that have been applied in many diverse fields with great success. In our previous work, we used a Prm-based technique to study protein folding pathways of several small proteins and obtained encouraging results. In this paper, we describe how our motion planning framework can be used to study protein folding kinetics. In particular, we present a refined version of our Prm-based framework and describe how it can be used to produce potential energy landscapes, free energy landscapes, and many folding pathways all from a single roadmap which is computed in a few hours on a desktop PC. Results are presented for 14 proteins. Our ability to produce large sets of unrelated folding pathways may potentially provide crucial insight into some aspects of folding kinetics, such as proteins that exhibit both two-state and three-state kinetics, that are not captured by other theoretical techniques.

Multithreaded Asynchronous Graph Traversal for In-Memory and Semi-External Memory

Processing large graphs is becoming increasingly important for many domains such as social networks, bioinformatics, etc. Unfortunately, many algorithms and implementations do not scale with increasing graph sizes. As a result, researchers have attempted to meet the growing data demands using parallel and external memory techniques. We present a novel asynchronous approach to compute Breadth-First-Search (BFS), Single-Source-Shortest-Paths, and Connected Components for large graphs in shared memory. Our highly parallel asynchronous approach hides data latency due to both poor locality and delays in the underlying graph data storage. We present an experimental study applying our technique to both In-Memory and Semi-External Memory graphs utilizing multi-core processors and solid-state memory devices. Our experiments using synthetic and real-world datasets show that our asynchronous approach is able to overcome data latencies and provide significant speedup over alternative approaches. For example, on billion vertex graphs our asynchronous BFS scales up to 14x on 16-cores.

A motion-planning approach to folding: from paper craft to protein folding

We present a framework for studying folding problems from a motion planning perspective. Modeling foldable objects as tree-like multi-link objects allows one to apply motion planning techniques to folding problems. An important feature of this approach is that it not only allows one to study foldability questions, such as, can an object be folded (or unfolded) into another object, but also provides one with another tool for investigating the dynamic folding process itself. The framework proposed here has application to traditional motion planning areas such as automation and animation, and presents a novel approach for studying protein folding pathways. Preliminary experimental results with traditional paper crafts (e.g., box folding) and small proteins (approximately 60 residues) are quite encouraging.

Parallel algorithms for higher-dimensional convex hulls

We give fast randomized and deterministic parallel methods for constructing convex hulls in R/sup d/, for any fixed d. Our methods are for the weakest shared-memory model, the EREW PRAM, and have optimal work bounds (with high probability for the randomized methods). In particular, we show that the convex hull of n points in R/sup d/ can be constructed in O(log n) time using O(n log n+n/sup [d/2]/) work, with high probability. We also show that it can be constructed deterministically in O(log/sup 2/ n) time using O(n log n) work for d=3 and in O(log n) time using O(n/sup [d/2]/ log/sup c([d/2]-[d/2]/) n) work for d/spl ges/4, where c>0 is a constant which is optimal for even d/spl ges/4. We also show how to make our 3-dimensional methods output-sensitive with only a small increase in running time. These methods can be applied to other problems as well.<<ETX>>

An obstacle-based rapidly-exploring random tree

Tree-based path planners have been shown to be well suited to solve various high dimensional motion planning problems. Here we present a variant of the Rapidly-Exploring Random Tree (RRT) path planning algorithm that is able to explore narrow passages or difficult areas more effectively. We show that both workspace obstacle information and C-space information can be used when deciding which direction to grow. The method includes many ways to grow the tree, some taking into account the obstacles in the environment. This planner works best in difficult areas when planning for free flying rigid or articulated robots. Indeed, whereas the standard RRT can face difficulties planning in a narrow passage, the tree based planner presented here works best in these areas