Abstract
Mobile Robot Indoor Positioning System has wide application in the industry and home automation field. Unfortunately, existing mobile robot indoor positioning methods often suffer from poor positioning accuracy, system instability, and need for extra installation efforts. In this paper, we propose a novel positioning system which applies the centralized positioning method into the mobile robot, in which real-time positioning is achieved via interactions between ARM and computer. We apply the Kernel extreme learning machine (K-ELM) algorithm as our positioning algorithm after comparing four different algorithms in simulation experiments. Real-world indoor localization experiments are conducted, and the results demonstrate that the proposed system can not only improve positioning accuracy but also greatly reduce the installation efforts since our system solely relies on Wi-Fi devices.
Highlights
With the development of the global positioning system (GPS), extremely high accuracy and reliable positioning have been achieved in most outdoor environments
To evaluate the positioning performance of the Kernel extreme learning machine (K-extreme learning machine (ELM))-based positioning system, KNN (k-nearest neighbor) [27], BYS (Bayesian) [28, 29], classic ELM [30], and OS-ELM [31] algorithms are utilized for comparison; we compared the above algorithms’ experimental result with the results of K-ELM in the positioning system
After analyzing the error statistics histogram, we know that the K-ELM algorithm has a smaller positioning error than others, and its distribution is more concentrated and its stability is better
Summary
With the development of the global positioning system (GPS), extremely high accuracy and reliable positioning have been achieved in most outdoor environments. Simultaneous localization and mapping (SLAM) is applied to the autonomous navigation and positioning of the robot It employs a large number of sensors such as vision, laser, and odometer to assist in positioning and constructing maps in unfamiliar environments [17,18,19]. Our system is useful for purposes of convenience and entertainment in homes and offices where localization error of robots is not catastrophic: for instance, robots for location finding and explanation in the museum, position detection of medical staff or equipment in hospitals, and finding tagged maintenance tools and equipment scattered all over the smart plant [20] Both theoretical analysis and experiments are carried out to verify the capability and demonstrate the superiority of the proposed system.
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