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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Vydáno v:	IEEE transactions on parallel and distributed systems Ročník 27; číslo 10; s. 2926 - 2939
Hlavní autoři:	Chretien, Benjamin, Escande, Adrien, Kheddar, Abderrahmane
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.10.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Témata:	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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Abstract	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.
AbstractList	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.
Author	Chretien, Benjamin Escande, Adrien Kheddar, Abderrahmane
Author_xml	– sequence: 1 givenname: Benjamin surname: Chretien fullname: Chretien, Benjamin email: chretien.b@gmail.com organization: Interactive Digital Human Group, LIRMM, Montpellier, France – sequence: 2 givenname: Adrien surname: Escande fullname: Escande, Adrien email: adrien.escande@gmail.com organization: AIST JRL, Tsukuba, Japan – sequence: 3 givenname: Abderrahmane surname: Kheddar fullname: Kheddar, Abderrahmane email: kheddar@gmail.com organization: Interactive Digital Human Group, LIRMM, Montpellier, France
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SubjectTerms	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
Title	GPU Robot Motion Planning Using Semi-Infinite Nonlinear Programming
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