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Massively parallel motion planning algorithms under uncertainty using POMDP

Title
Massively parallel motion planning algorithms under uncertainty using POMDP
Authors
Lee, TaekheeKim, Young J.
Ewha Authors
김영준이택희
SCOPUS Author ID
김영준scopus
Issue Date
2016
Journal Title
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
ISSN
0278-3649JCR Link1741-3176JCR Link
Citation
vol. 35, no. 8, pp. 928 - 942
Keywords
POMDPMonte Carlo value iterationGPUCPU-GPUgPOMDPhPOMDP
Publisher
SAGE PUBLICATIONS LTD
Indexed
SCI; SCIE; SCOPUS WOS scopus
Abstract
We present new parallel algorithms that solve continuous-state partially observable Markov decision process (POMDP) problems using the GPU (gPOMDP) and a hybrid of the GPU and CPU (hPOMDP). We choose the Monte Carlo value iteration (MCVI) method as our base algorithm and parallelize this algorithm using the multi-level parallel formulation of MCVI. For each parallel level, we propose efficient algorithms to utilize the massive data parallelism available on modern GPUs. Our GPU-based method uses the two workload distribution techniques, compute/data interleaving and workload balancing, in order to obtain the maximum parallel performance at the highest level. Here we also present a CPU-GPU hybrid method that takes advantage of both CPU and GPU parallelism in order to solve highly complex POMDP planning problems. The CPU is responsible for data preparation, while the GPU performs Monte Cacrlo simulations; these operations are performed concurrently using the compute/data overlap technique between the CPU and GPU. To the best of the authors' knowledge, our algorithms are the first parallel algorithms that efficiently execute POMDP in a massively parallel fashion utilizing the GPU or a hybrid of the GPU and CPU. Our algorithms outperform the existing CPU-based algorithm by a factor of 75-99 based on the chosen benchmark.
DOI
10.1177/0278364915594856
Appears in Collections:
엘텍공과대학 > 컴퓨터공학과 > Journal papers
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