Chance-constrained target tracking for mobile robots
Yoonseon Oh, Sungjoon Choi, Songhwai Oh
Abstract
This paper presents a robust target tracking algorithm for a mobile sensor with a fan-shaped field of view and finite sensing range. The goal of the mobile robot is to track a moving target such that the probability of losing the target is minimized. We assume that the distribution of the next position of a moving target can be estimated using a motion prediction algorithm. If the next position of a moving target has the Gaussian distribution, the proposed algorithm can guarantee the tracking success probability. In addition, the proposed method minimizes the moving distance of the mobile robot based on a bound on the tracking success probability. While the problem considered in this paper is a non-convex optimization problem, we derive analytical solutions which can be easily solved in real-time. The performance of the proposed method is evaluated extensively in simulation and validated in pedestrian following experiments using a Pioneer mobile robot with a Microsoft Kinect sensor.
BibTeX
@inproceedings{icra2015_chanceconstraine,
title = {Chance-constrained target tracking for mobile robots},
author = {Yoonseon Oh and Sungjoon Choi and Songhwai Oh},
booktitle = {ICRA 2015},
year = {2015}
}