The STAPL parallel container framework

Tanase, Gabriel; Buss, Antal; Fidel, Adam; Harshvardhan,; Papadopoulos, I.; Pearce, Olga; Smith, Timmie; Thomas, Nathan; Xu, Xinyi; Mourad, Nedal; Vu, Jeremy; Bianco, Mauro; Amato, Nancy M.; Rauchwerger, Lawrence

doi:10.1145/2038037.1941586

Cited by 22 publications

(19 citation statements)

References 21 publications

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“…This library uses a distributed graph data structure from the Standard Template Adaptive Parallel Library (STAPL) [25], a C++ library designed for parallel computing. However, experiments were all done sequentially, not in parallel.…”

Section: A Experimental Setupmentioning

confidence: 99%

Spark PRM: Using RRTs within PRMs to efficiently explore narrow passages

Shi

Denny

Amato

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Probabilistic RoadMaps (PRMs) have been successful for many high-dimensional motion planning problems. However, they encounter difficulties when mapping narrow passages. While many PRM sampling methods have been proposed to increase the proportion of samples within narrow passages, such difficult planning areas still pose many challenges. We introduce a novel algorithm, Spark PRM, that sparks the growth of Rapidly-expanding Random Trees (RRTs) from narrow passage samples generated by a PRM. The RRT rapidly generates further narrow passage samples, ideally until the passage is fully mapped. After reaching a terminating condition, the tree stops growing and is added to the roadmap. Spark PRM is a general method that can be applied to all PRM variants. We study the benefits of Spark PRM with a variety of sampling strategies in a wide array of environments. We show significant speedups in computation time over RRT, Sampling-based Roadmap of Trees (SRT), and various PRM variants.

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Section: A Experimental Setupmentioning

confidence: 99%

Spark PRM: Using RRTs within PRMs to efficiently explore narrow passages

Shi

Denny

Amato

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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“…Our code was written in C++ using the Standard Template Library (STL) and the Standard Template Adaptive Parallel Library (STAPL) [6], [25] as supporting libraries. STAPL is a platform independent superset of STL that provides a collection of building blocks for writing parallel programs.…”

Section: A Stapl Frameworkmentioning

confidence: 99%

A scalable distributed RRT for motion planning

Jacobs

Stradford

Rodríguez

et al. 2013

2013 IEEE International Conference on Robotics and Automation

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Abstract-Rapidly-exploring Random Tree (RRT), like other sampling-based motion planning methods, has been very successful in solving motion planning problems. Even so, samplingbased planners cannot solve all problems of interest efficiently, so attention is increasingly turning to parallelizing them. However, one challenge in parallelizing RRT is the global computation and communication overhead of nearest neighbor search, a key operation in RRTs. This is a critical issue as it limits the scalability of previous algorithms. We present two parallel algorithms to address this problem. The first algorithm extends existing work by introducing a parameter that adjusts how much local computation is done before a global update. The second algorithm radially subdivides the configuration space into regions, constructs a portion of the tree in each region in parallel, and connects the subtrees, removing cycles if they exist. By subdividing the space, we increase computation locality enabling a scalable result. We show that our approaches are scalable. We present results demonstrating almost linear scaling to hundreds of processors on a Linux cluster and a Cray XE6 machine.

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“…This distance metric is generally considered to be very accurate at the expense of being costly to compute [3]. c) Implementation and Experimental Platform: We implemented all planners using the C++ motion planning library developed by the Parasol Lab at Texas A&M University, which uses the graph from the STAPL Parallel C++ library [32]. RAPID [16] was used for collision detection computations.…”

Section: B Setupmentioning

confidence: 99%

“…In this part of our study we used 4 k values (4,8,16,32). These values were selected because they were representative of the range of k values commonly used in motion planning problems.…”

Section: A Selecting Parameters For Localrandmentioning

confidence: 99%

Local randomization in neighbor selection improves PRM roadmap quality

McMahon

Jacobs

Boyd

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-Probabilistic Roadmap Methods (PRMs) are one of the most used classes of motion planning methods. These sampling-based methods generate robot configurations (nodes) and then connect them to form a graph (roadmap) containing representative feasible pathways. A key step in PRM roadmap construction involves identifying a set of candidate neighbors for each node. Traditionally, these candidates are chosen to be the k-closest nodes based on a given distance metric. In this paper, we propose a new neighbor selection policy called LocalRand(k, k ′ ), that first computes the k ′ closest nodes to a specified node and then selects k of those nodes at random. Intuitively, LocalRand attempts to benefit from random sampling while maintaining the higher levels of local planner success inherent to selecting more local neighbors. We provide a methodology for selecting the parameters k and k ′ . We perform an experimental comparison which shows that for both rigid and articulated robots, LocalRand results in roadmaps that are better connected than the traditional k-closest policy or a purely random neighbor selection policy. The cost required to achieve these results is shown to be comparable to k-closest. I. IntroductionThe motion planning problem involves finding a valid path for a movable object (robot) from a start to a goal configuration in a given environment. Motion planning is an important component of many applications, including computer-aided design Sampling-based methods have been able to solve many motion planning problems that exact methods cannot. One of the most common randomized methods is the Probabilistic Roadmap Method, or PRM [19]. This method randomly generates valid samples (nodes) in an environment's configuration space (C-space) and then attempts to connect nearby pairs of nodes using a local planner, a simplified planner whose objective is to generate and validate transitions between the the specified pair of nodes. The resulting graph, or roadmap, encodes representative feasible paths in C-space and can be queried to obtain valid paths in the environment.One of the key steps in PRM construction is node connection. Ideally, roadmap connectivity should reflect the connectivity of the underlying C-space. From this perspective, the best strategy would be to attempt to connect all θ (n 2 ) pairs of nodes. However, the cost of all these connection attempts is not feasible for any but the simplest of problems. Hence, the selection of candidates for local transitions (neighbors) is crucial to both roadmap quality and efficiency.The objective of a good neighbor selection strategy is to identify pairs of configurations that have a high probability of being connectible by the local planner and that are useful in terms of producing good quality roadmaps. The most commonly used method for neighbor selection in PRMs uses nearest-neighbor search to select the k nodes that are closest to the node in question, where k is typically some relatively small, fixed constant, typically between 5 and 25 [10].

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The STAPL parallel container framework

Cited by 22 publications

References 21 publications

Spark PRM: Using RRTs within PRMs to efficiently explore narrow passages

Spark PRM: Using RRTs within PRMs to efficiently explore narrow passages

A scalable distributed RRT for motion planning

Local randomization in neighbor selection improves PRM roadmap quality

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