Artificial neural network-based relative positioning method and device, for recovering lost distance information in environment where fixed node is not present and all nodes are mobile

An artificial neural network-based method and device restore lost distance information between moving nodes, addressing positioning challenges in dynamic environments without fixed nodes, ensuring accurate and reliable robot system operation.

WO2026121377A1 Publication Date: 2026-06-11HANBAT NAT UNIV IND ACADEMIC COOPERATION FOUND
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Patent Information

Application Number
PCT/KR2024/020018
Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
Priority Date
2024-12-06
Filing Date
2024-12-09
Publication Date
2026-06-11

AI Technical Summary

Technical Problem

Existing technologies face challenges in accurately determining the positions of mobile nodes in dynamic environments without fixed reference nodes, particularly when GPS is unavailable or unreliable, and there is a risk of losing distance information due to signal loss or obstacles.

Method used

An artificial neural network-based method and device that collects and restores lost distance information between moving nodes, using interpolation techniques and artificial intelligence to estimate relative coordinates, even in environments without fixed nodes.

Benefits of technology

Enables accurate positioning of mobile nodes by restoring lost distance information, enhancing system stability and flexibility, and improving the reliability of robot systems in environments where GPS is impossible or restricted.

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Abstract

The present invention relates to an artificial neural network-based relative positioning method and device, for recovering lost distance information in an environment where a fixed node is not present and all nodes are mobile, the artificial neural network-based relative positioning method, according to one embodiment of the present invention, comprising the steps of: for a plurality of nodes arranged into a formation, collecting distance information between each of the nodes; checking whether a loss has occurred in the collected distance information, and recovering the lost distance information in the collected distance information; storing distance data including the distance information obtained by the recovering of the lost distance information in the collected distance information; training a first artificial neural network by using the stored distance data as input; and predicting the formation by estimating relative coordinate information of each of the nodes on the basis of the trained artificial neural network.
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