Study of Variational Inference for Flexible Distributed Probabilistic Robotics

Malte Rørmose Damgaard; Rasmus Pedersen; Thomas Bak

doi:10.20944/preprints202202.0053.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 2 February 2022 / Approved: 3 February 2022 / Online: 3 February 2022 (13:48:14 CET)

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

Computer Science and Mathematics, Robotics

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Study of Variational Inference for Flexible Distributed Probabilistic Robotics

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