GPU Robot Motion Planning Using Semi-Infinite Nonlinear Programming

We propose a many-core GPU implementation of robotic motion planning formulated as a semi-infinite optimization program. Our approach computes the constraints and their gradients in parallel, and feeds the result to a nonlinear optimization solver running on the CPU. To ensure the continuous satisfa...

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Vydané v:IEEE transactions on parallel and distributed systems Ročník 27; číslo 10; s. 2926 - 2939
Hlavní autori: Chretien, Benjamin, Escande, Adrien, Kheddar, Abderrahmane
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.10.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Predmet:
Computer Science
CUDA
Distributed, Parallel, and Cluster Computing
GPGPU
Graphics processing units
HPC
Kinematics
motion planning
nonlinear optimization
Optimization
parallel computing
Planning
Robot kinematics
Robotics
Service robots
CUDA
motion planning
robotics
HPC
parallel computing
GPGPU
nonlinear optimization
ISSN:1045-9219, 1558-2183
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Popis
Shrnutí:We propose a many-core GPU implementation of robotic motion planning formulated as a semi-infinite optimization program. Our approach computes the constraints and their gradients in parallel, and feeds the result to a nonlinear optimization solver running on the CPU. To ensure the continuous satisfaction of our constraints, we use polynomial approximations over time intervals. Because each constraint and its gradient can be evaluated independently for each time interval, we end up with a highly parallelizable problem that can take advantage of many-core architectures. Classic robotic computations (geometry, kinematics, and dynamics) can also benefit from parallel processors, and we carefully study their implementation in our context. This results in having a full constraint evaluator running on the GPU. We present several optimization examples with a humanoid robot. They reveal substantial improvements in terms of computation performance compared to a parallel CPU version.
Bibliografia:ObjectType-Article-1
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ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2016.2521373