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Title: Optical localization of a mobile robot using sensitivity-based data fusion
Optical communication is of increasing interest as an alternative to acoustic communication for robots operated in underwater environments. Our previous work presented a method for LED-based Simultaneous Localization and Communication (SLAC) that uses the bearing angles, obtained in establishing line-of-sight (LOS) between two beacon nodes and a mobile robot for communication, for geometric triangulation and thus localization of the robot. In this paper, we consider the problem of optical localization in the setting of a network of beacon nodes, and specifically, how to fuse the measurements from multiple pairs of beacon nodes to obtain the target location. A sensitivity metric, which represents how sensitive the target estimate is with respect to the bearing measurement errors, is used for selecting the desired pair of beacon nodes. The proposed solution is evaluated with extensive simulation and preliminary experimentation, in a setting of three beacon nodes and one mobile node. Comparison with an average-based fusion approach and an approach using a fixed pair of beacon nodes demonstrates the efficacy of the proposed approach.
Award ID(s):
1734272 1446793
Publication Date:
Journal Name:
Proceedings of 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics
Page Range or eLocation-ID:
Sponsoring Org:
National Science Foundation
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    Localization of mobile robots is essential for navigation and data collection. This work presents an optical localization scheme for mobile robots during the robot’s continuous movement, despite that only one bearing angle can be captured at a time. In particular, this paper significantly improves upon our previous works where the robot has to pause its movement in order to acquire the two bearing angle measurements needed for position determination. The latter restriction forces the robot to work in a stop-and-go mode, which constrains the robot’s mobilitty. The proposed scheme exploits the velocity prediction from Kalman filtering, to properly correlate two consecutive measurements of bearing angles with respect to the base nodes (beacons) to produce location measurement. The proposed solution is evaluated in simulation and its advantage is demonstrated through the comparison with the traditional approach where the two consecutive angle measurements are directly used to compute the location.

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