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Autonomous Robots
Erschienen in: Autonomous Robots 2/2018

18.07.2017

Chance-constrained target tracking using sensors with bounded fan-shaped sensing regions

verfasst von: Yoonseon Oh, Sungjoon Choi, Songhwai Oh

Erschienen in: Autonomous Robots | Ausgabe 2/2018

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Abstract

We present a robust target tracking algorithm for a mobile robot. It is assumed that a mobile robot carries a sensor with a fan-shaped field of view and finite sensing range. The goal of the proposed tracking algorithm is to minimize the probability of losing a target. If the distribution of the next position of a moving target is available as a Gaussian distribution from a motion prediction algorithm, the proposed algorithm can guarantee the tracking success probability. In addition, the proposed method minimizes the moving distance of the mobile robot based on the chosen bound on the tracking success probability. While the considered problem is a non-convex optimization problem, we derive a closed-form solution when the heading is fixed and develop a real-time algorithm for solving the considered target tracking problem. We also present a robust target tracking algorithm for aerial robots in 3D. The performance of the proposed method is evaluated extensively in simulation. The proposed algorithm has been successful applied in field experiments using Pioneer mobile robot with a Microsoft Kinect sensor for following a pedestrian.
Vorheriger Artikel Receding horizon path planning for 3D exploration and surface inspection
Nächster Artikel Near-optimal probabilistic search using spatial Fourier sparse set

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Fußnoten
2
Vicon MX motion capture system. Available at http://​www.​vicon.​com/​.
 
3
To generate the same walking pattern for all 10 trials, we slow down all the settings of the system.
 
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Metadaten
Titel
Chance-constrained target tracking using sensors with bounded fan-shaped sensing regions
verfasst von
Yoonseon Oh
Sungjoon Choi
Songhwai Oh
Publikationsdatum
18.07.2017
Verlag
Springer US
Erschienen in
Autonomous Robots / Ausgabe 2/2018
Print ISSN: 0929-5593
Elektronische ISSN: 1573-7527
DOI
https://doi.org/10.1007/s10514-017-9656-7

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