ASC localization in noisy environment based on wireless sensor network

Abstract

This study investigates indoor localization problem of robot or a customer at shopping mall environment. To improve the localization accuracy, a sensor fusion-based approach is employed, which combines data from ZigBee, odometry of active shopping cart (ASC), and QR marker. The proposed algorithm employs Gaussian probability estimation method and thus it is adaptive to localization problem even at noisy environment such as the shopping mall. To implement the localization service, an ASC which is equipped with motors for navigation, a laser sensor for tracking, and a tablet computer for human-computer interaction is designed. Through experimental work, we corroborate the feasibility of the proposed localization algorithms.

Multi-group localization problem of service robots based on hybrid external localization algorithm with application to shopping mall environment

“…The multi-cart system is much complex as compared with a single cart problem [30]. As mentioned in Sect.…”

“…3 is designed for mobility. The detail about DDTM was introduced in [30]. Besides, a camera is installed at the bottom of the cart to detect QR marker and LED lights are fixed on the DDTM for better illumination.…”

“…A laser range finder (LRF) has been widely used to observe environment and detect objects in many robotic systems [30][31][32][33]. Jung et al [33] employed LRF to detect a human body.…”

“…10. Different from previous work [30], in this paper, we add posterior probability computing to improve ZigBee location accuracy. When the measurement X t is received, the probability of area A i is calculated by Bayes theorem as [36] …”

“…Gai et al [30] proposed a localization problem of a single cart. This paper will extend it to multi-group robot problem, which is based on a hybrid wireless and laser range finder (LRF) sensory fusion system.…”