Active Acoustic Source Tracking Exploiting Particle Filtering and Monte Carlo Tree Search

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Details zur Publikation

Autorinnen und Autoren: Haubner T, Schmidt A, Kellermann W
Jahr der Veröffentlichung: 2019
Sprache: Englisch


Abstract

In this paper, we address the task of active acoustic source tracking as part of robotic path planning. It denotes the planning of sequences of robotic movements to enhance tracking results of acoustic sources, e.g., talking humans, by fusing observations from multiple positions. Essentially, two strategies are
possible: short-term planning, which results in greedy behavior, and long-term planning, which considers a sequence of possible future movements of the robot and the source. Here, we focus on
the second method as it might improve tracking performance compared to greedy behavior and propose a path planning algorithm which exploits Monte Carlo Tree Search (MCTS) and particle filtering, based on a reward motivated by information-theoretic considerations. By representing the state posterior by weighted particles, we are capable of modelling arbitrary probability density functions (PDF)s and dealing with highly nonlinear state-space models.


FAU-Autorinnen und Autoren / FAU-Herausgeberinnen und Herausgeber

Haubner, Thomas
Professur für Nachrichtentechnik
Kellermann, Walter Prof. Dr.-Ing.
Professur für Nachrichtentechnik
Schmidt, Alexander
Professur für Nachrichtentechnik


Zitierweisen

APA:
Haubner, T., Schmidt, A., & Kellermann, W. (2019). Active Acoustic Source Tracking Exploiting Particle Filtering and Monte Carlo Tree Search. In Proceedings of the 27th European Signal Processing Conference, EUSIPCO 2019. A Coruña, ES.

MLA:
Haubner, Thomas, Alexander Schmidt, and Walter Kellermann. "Active Acoustic Source Tracking Exploiting Particle Filtering and Monte Carlo Tree Search." Proceedings of the 27th European Signal Processing Conference, EUSIPCO 2019, A Coruña 2019.

BibTeX: 

Zuletzt aktualisiert 2019-11-07 um 14:08