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

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, airports, or for evacuation of disaster zones. Many methodologies have been explored to solve this issue, including the implementation of a variety of sensors and other signal receiving systems. Most interestingly, camera-based techniques have become more popular in this kind of scenarios, given their robustness and scalability. In this paper, we implement 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 capable of solving 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 showcase some initial experimentation in a pair of robots with different mechanical characteristics. Further implementations of this work include communicating the robots for other tasks, such as teaching assistance, remote classes, and other innovations in higher education.

Remote Big Data Management Tools, Sensing and Computing Technologies, and Visual Perception and Environment Mapping Algorithms in the Internet of Robotic Things

“…[ [25][26][27][28][29][30][31][32] Context-aware and semantic IoRT systems monitor events, manipulate connected objects, and fuse sensor data by leveraging action modeling and distributed intelligence tools. IoRT-based monitored and mobile edge computing environments integrate sensor-based communication networks and fog computing technologies.…”

“…Collaborative autonomous multi-robot systems perform tasks in flexible industrial environments [25][26][27][28] through the use of image processing and data acquisition tools, computer vision and object recognition algorithms, and robotic guidance technologies. Robot vision and navigation systems are pivotal in reconfigurable manufacturing processes across intelligent simulation environments and smart factories.…”

“…Data acquisition and signal processing tools, convolutional neural networks, and obstacle avoidance algorithms [21][22][23][24] enable image and speech recognition processes. Computation-enabled robotic devices control and monitor connected objects and sensor networks [25][26][27][28] by use of computer vision and machine learning algorithms, artificial neural networks, and deep reinforcement learning tools. Data fusion and cloud computing technologies assist autonomous robotic systems through localization accuracy [29][30][31][32] with regards to multiple sound sources across ambient sensing environments and distributed sensor networks.…”