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Autonomous Robots
Erschienen in: Autonomous Robots 3-4/2020

20.06.2019

A robust localization system for multi-robot formations based on an extension of a Gaussian mixture probability hypothesis density filter

verfasst von: Alicja Wasik, Pedro U. Lima, Alcherio Martinoli

Erschienen in: Autonomous Robots | Ausgabe 3-4/2020

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Abstract

This paper presents a strategy for providing reliable state estimates that allow a group of robots to realize a formation even when communication fails and the tracking data alone is insufficient for maintaining a stable formation. Furthermore, the tracking information does not provide the identity of the robot, therefore a simple fusion of tracking and communication data is not possible. We extend a Gaussian mixture probability hypothesis density filter to incorporate, firstly, absolute poses exchanged by the robots, and secondly, the geometry of the desired formation. Our method of combining communicated data, information about the formation and sensory detections is capable of maintaining the state estimates even when long-duration occlusions occur, and improves awareness of the situation when the communication is sporadic or suffers from short-term outage. The proposed method is validated using a high-fidelity simulator in scenarios with a formation of up to five robots. The results show that the proposed tracking strategy allows for sustaining formations in cluttered environments, with high measurement uncertainty and low quality communication.
Vorheriger Artikel SOUL: data sharing for robot swarms
Nächster Artikel On-board range-based relative localization for micro air vehicles in indoor leader–follower flight

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Fußnoten
1
Analogously to the single-target system, where uncertainty is expressed by modeling the state \(\mathbf {x}_k\) and the measurement \(\mathbf {z}_k\) as random vectors, the uncertainty of the multi-target system is expressed by modelling the multi-target state \(X_k\) and the multi-target measurement \(Z_k\) as RFS.
 
2
The local maxima of the intensity v are the local concentrations of the expected number of elements and can be used to estimate the elements of X.
 
3
The robots self-localize on a known map, therefore the orientation of the body with respect to the map frame is estimated online.
 
5
Wasik et al. (2016b) uses sliding window, nearest neighbor classification for that purpose.
 
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Metadaten
Titel
A robust localization system for multi-robot formations based on an extension of a Gaussian mixture probability hypothesis density filter
verfasst von
Alicja Wasik
Pedro U. Lima
Alcherio Martinoli
Publikationsdatum
20.06.2019
Verlag
Springer US
Erschienen in
Autonomous Robots / Ausgabe 3-4/2020
Print ISSN: 0929-5593
Elektronische ISSN: 1573-7527
DOI
https://doi.org/10.1007/s10514-019-09860-5

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