High-dimensional planning on the GPU

Kider, Joseph T.; Henderson, Mark; Likhachev, Maxim; Safonova, Alla

doi:10.1109/robot.2010.5509470

Cited by 34 publications

(25 citation statements)

References 10 publications

(12 reference statements)

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“…E. Plaku, et al [19] have shown that RoadMap motion planning algorithms may have efficient parallel versions, up to almost linear speedup. Further runtime improvement can be achieved using a GPGPU approach for motion planing (see [20], [21]) One can apply the SIMD (Single Instruction Multiple Data) methodology over Algorithm 2. Recall that Algorithm 2 determines the connectivity between all pairs of anchoring points and therefore is suitable for parallel computing (requires limited size of data).…”

Section: Algorithm 2: Crawling Probabilistic Road Mapmentioning

confidence: 99%

A real-time motion planning algorithm for a hyper-redundant set of mechanisms

2013

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ABSTRACT. We introduce a novel probabilistic algorithm (CPRM) for real-time motion planning in the configuration space C. Our algorithm differs from a Probabilistic Road Map algorithm (PRM) in the motion between a pair of anchoring points (local planner) which takes place on the boundary of the obstacle subspace O. We define a varying potential field f on ∂O as a Morse function and follow ∇f . We then exemplify our algorithm on a redundant worm climbing robot with n degrees of freedom and compare our algorithm running results with those of PRM.

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Section: Algorithm 2: Crawling Probabilistic Road Mapmentioning

confidence: 99%

A real-time motion planning algorithm for a hyper-redundant set of mechanisms

2013

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“…With robotics applications as main focus, Kider et al [43] implement a GPU version of R*, a randomized, non-exact version of the A* algorithm called R*GPU. They report that R*GPU consistently produces lower cost solutions, scales better in terms of memory, and runs faster than R*.…”

Section: Gpu Computing For Shortest Path Problemsmentioning

confidence: 99%

GPU computing in discrete optimization. Part II: Survey focused on routing problems

Schulz

Hasle

Brodtkorb

et al. 2013

EURO Journal on Transportation and Logistics

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In many cases there is still a large gap between the performance of current optimization technology and the requirements of real world applications. As in the past, performance will improve through a combination of more powerful solution methods and a general performance increase of computers. These factors are not independent. Due to physical limits, hardware development no longer results in higher speed for sequential algorithms, but rather in increased parallelism. Modern commodity PCs include a multi-core CPU and at least one GPU, providing a low cost, easily accessible heterogeneous environment for high performance computing. New solution methods that combine task parallelization and stream processing are needed to fully exploit modern computer architectures and profit from future hardware developments. This paper is the second in a series of two. Part I gives a tutorial style introduction to modern PC architectures and GPU programming. Part II gives a broad survey of the literature on parallel computing in discrete optimization targeted at modern PCs, with special focus on routing problems. We assume that the reader is familiar with GPU programming, and refer the interested reader to Part I. We conclude with lessons learnt, directions for future research, and prospects.

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“…Foskey et al also used GPUs to accelerate hybrid planners based on sampling and Voronoi diagrams [12]. Kider et al [13] created a GPU-based planner for R*, a randomized version of A*, that retains the theoretical properties of R* but with improved experimental results.…”

Section: Related Workmentioning

confidence: 99%

Parallel sampling-based motion planning with superlinear speedup

Ichnowski

Alterovitz

2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-We present PRRT (Parallel RRT) and PRRT* (Parallel RRT*), sampling-based methods for feasible and optimal motion planning that are tailored to execute on modern multi-core CPUs. Our algorithmic improvements enable PRRT and PRRT* to achieve a superlinear speedup: when p processor cores are used instead of 1 processor core, computation time is sped up by a factor greater than p. To achieve this superlinear speedup, our algorithms utilize three key features: (1) lock-free parallelism using atomic operations to eliminate slowdowns caused by lock overhead and contention, (2) partition-based sampling to reduce the size of each processor core's working data set to improve cache efficiency, and (3) parallel backtracking to reduce the number of rewiring steps performed in PRRT*. Our parallel algorithms retain the ability to integrate with existing CPU-based libraries and algorithms. We demonstrate fast performance and superlinear speedups in two scenarios: (1) a holonomic disc-shaped robot moving in a planar environment and (2) an Aldebaran Nao small humanoid robot performing a 2-handed manipulation task using 10 DOF.

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High-dimensional planning on the GPU

Cited by 34 publications

References 10 publications

A real-time motion planning algorithm for a hyper-redundant set of mechanisms

A real-time motion planning algorithm for a hyper-redundant set of mechanisms

GPU computing in discrete optimization. Part II: Survey focused on routing problems

Parallel sampling-based motion planning with superlinear speedup

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