Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Implementation of NAO Robot Maze Navigation Based on Computer Vision and Collaborative Learning

Version 1 : Received: 31 May 2021 / Approved: 1 June 2021 / Online: 1 June 2021 (14:49:11 CEST)

How to cite: Magallán-Ramirez, D.; Rodriguez-Tirado, A.; Martínez-Aguilar, J.D.; Moreno-García, C.F.; Balderas, D.; López-Caudana, E.O. Implementation of NAO Robot Maze Navigation Based on Computer Vision and Collaborative Learning. Preprints 2021, 2021060037 (doi: 10.20944/preprints202106.0037.v1). Magallán-Ramirez, D.; Rodriguez-Tirado, A.; Martínez-Aguilar, J.D.; Moreno-García, C.F.; Balderas, D.; López-Caudana, E.O. Implementation of NAO Robot Maze Navigation Based on Computer Vision and Collaborative Learning. Preprints 2021, 2021060037 (doi: 10.20944/preprints202106.0037.v1).

Abstract

Maze navigation using one or more robots has become a recurring challenge in scientific literature and real life practice, with fleets having to find faster and better ways to navigate environments such as a travel hub (e.g. airports) or to evacuate a disaster zone. Many methods have been used to solve this issue, including the implementation of a variety of sensors and other signal receiving systems. Most interestingly, camera-based techniques have increasingly become more popular in this kind of applications, given their robustness and scalability. In this paper, we have implemented an end-to-end strategy to address this scenario, allowing a robot to solve a maze in an autonomous way, by using computer vision and path planning. In addition, this robot shares the generated knowledge to another by means of communication protocols, having to adapt its mechanical characteristics to be able to solve the same challenge. The paper presents experimental validation of the four components of this solution, namely camera calibration, maze mapping, path planning and robot communication. Finally, we present the integration and functionality of these methods applied in a pair of NAO robots.

Subject Areas

robot navigation; computer vision; camera calibration; mapping; path planning; communication; NAO robot; educational innovation; higher education

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