Abstract
Mobile robot localizations have been extensively studied, and various algorithms for multiple-robot localization have been developed. However, existing methods for multiple-robot localization often exhibit poor performance under harsh conditions, such as missing measurements and sudden appearance of obstacles. To overcome this problem, this paper proposes a novel method for multiple-robot localization in wireless sensor networks. The proposed method is theoretically based on the finite memory estimation and utilizes relative distance and angle measurements between robots. Thus, the proposed method is referred to as distributed finite memory estimation from relative measurements (DFMERM). Due to the finite memory structure, the DFMERM has inherent robustness against computational and modeling errors. Moreover, the novel distributed localization method using relative measurements shows the robustness against missing measurements. Robust DFMERM localization performance is experimentally demonstrated using multiple mobile robots under the harsh conditions.
Highlights
Real-time locating systems (RTLS) based on wireless sensor networks (WSN) are popular in various industrial fields, such as smart factories, public facilities, and logistics facilities [1]–[7]
RTLS suffer from localization failure due to missing measurements [8], [9], which are caused by communication and sensor errors
A novel localization algorithm that can cope with the missing measurement problem is required
Summary
Real-time locating systems (RTLS) based on wireless sensor networks (WSN) are popular in various industrial fields, such as smart factories, public facilities, and logistics facilities [1]–[7]. Kim et al.: DFMERM for Multiple-Robot Localization in WSN these algorithms are based on infinite impulse response (IIR) estimation, and they may exhibit estimation divergence due to the accumulation of computational or modeling errors. KFs suffer from filter divergence, which is caused by the accumulation of computational or modeling errors To overcome this problem, finite memory estimation (FME) algorithms [5], [12], [29]–[37] that use only recent finite measurements have been studied. We propose a novel distributed FME algorithm for multiple-robot localization; it can estimate the pose (i.e., position and heading angle) of all individual robots in a WSN. The technique for refining sensor measurements proposed in our previous study [31] is evolved and extended to the distributed multiple-robot localization problem.
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