Fog Robotics Algorithms for Distributed Motion Planning Using Lambda Serverless Computing
Article 2020 en
Authors
JI
Jeffrey Ichnowski
WL
William Lee
VM
Victor Murta
Abstract
1 min read
For robots using motion planning algorithms such as RRT and RRT*, the computational load can vary by orders of magnitude as the complexity of the local environment changes. To adaptively provide such computation, we propose Fog Robotics algorithms in which cloud-based serverless lambda computing provides parallel computation on demand. To use this parallelism, we propose novel motion planning algorithms that scale effectively with an increasing number of serverless computers. However, given that the allocation of computing is typically bounded by both monetary and time constraints, we show how prior learning can be used to efficiently allocate resources at runtime. We demonstrate the algorithms and application of learned parallel allocation in both simulation and with the Fetch commercial mobile manipulator using Amazon Lambda to complete a sequence of sporadically computationally intensive motion planning tasks.
Jeffrey Ichnowski, Kaiyuan Chen, Karthik Dharmarajan, Simeon Adebola, Michael Danielczuk, Víctor Mayoral-Vilches, Nikhil Jha, Hugo Zhan, Edith Llontop, Derek Xu, Camilo Buscaron, John Kubiatowicz, Ion Stoica, Joseph E. Gonzalez, Ken Goldberg
Jeffrey Ichnowski, Kaiyuan Chen, Karthik Dharmarajan, Simeon Adebola, Michael Danielczuk, Vıctor Mayoral-Vilches, Nikhil Jha, Hugo Zhan, Edith Llontop, Derek Xu, Camilo Buscaron, John Kubiatowicz, Ion Stoica, Joseph E. Gonzalez, Ken Goldberg
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