BLE Beacon-based floor detection for mobile robots in a multi-floor automation Laboratory

Author:

Wu Haiping1,Liu Hui1,Roddelkopf Thomas2,Thurow Kerstin3ORCID

Affiliation:

1. Institute of Artificial Intelligence & Robotics (IAIR), Key Laboratory of Traffic Safety on Track of Ministry of Education, School of Traffic and Transportation Engineering, Central South University , Changsha 410075, Hunan , China

2. Center of Life Science Automation (celisca) , 18119 Rostock, Germany

3. Institute of Automation (IAT), University Rostock , 18119 Rostock , Germany

Abstract

Abstract As an important task of multi-floor localization, floor detection has elicited great attention. Wireless infrastructures like Wi-Fi and Bluetooth low-energy play important roles in floor detection. However, most floor detection research studies tend to focus on data modeling but pay little attention to the data collection system, which is the basis of wireless infrastructure-based floor detection. In fact, the floor detection task can be greatly simplified with proper data collection system design. In this paper, a floor detection solution is developed in a multi-floor life science automation lab. A data collection system consisting of BLE beacons, receiver node, and IoT cloud is provided. The features of the BLE beacon under different settings are evaluated in detail. A mean filter is designed to deal with the fluctuation of the RSSI data. A simple floor detection method without a training process was implemented and evaluated in more than 100 floor detection tests. The time delay and floor detection accuracy under different settings are discussed. Finally, floor detection is evaluated on the H20 multi-floor transportation robot. Two sensor nodes are installed on the robot at different heights. The floor detection performance with different installation heights is discussed. The experimental results indicate that the proposed floor detection method provides floor detection accuracy of 0.9877 to 1 with a time delay of 5 s.

Publisher

Oxford University Press (OUP)

Subject

Engineering (miscellaneous),Safety, Risk, Reliability and Quality,Control and Systems Engineering

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Slope Landmark Classification for Indoor Vehicle Floor Detection;2024 IEEE International Conference on Consumer Electronics (ICCE);2024-01-06

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