Article Version 1 Preserved in Portico This version is not peer-reviewed
Study of Variational Inference for Flexible Distributed Probabilistic Robotics
Version 1 : Received: 2 February 2022 / Approved: 3 February 2022 / Online: 3 February 2022 (13:48:14 CET)
A peer-reviewed article of this Preprint also exists.
Damgaard, M.R.; Pedersen, R.; Bak, T. Study of Variational Inference for Flexible Distributed Probabilistic Robotics. Robotics 2022, 11, 38. Damgaard, M.R.; Pedersen, R.; Bak, T. Study of Variational Inference for Flexible Distributed Probabilistic Robotics. Robotics 2022, 11, 38.
By combining stochastic variational inference with message passing algorithms we show how to solve the highly complex problem of navigation and avoidance in distributed multi-robot systems in a computationally tractable manner, allowing online implementation. Subsequently, the proposed variational method lends itself to more flexible solutions than prior methodologies. Furthermore, the derived method is verified both through simulations with multiple mobile robots and a real world experiment with two mobile robots. In both cases the robots shares the operating space and needs to cross each other’s paths multiple times without colliding.
Distributed Robotics; Probabilistic Robotics; Variational Inference; Message-Passing Algorithm; Stochastic Variational Inference
MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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