Magallán-Ramírez Daniela, Martínez-Aguilar Jorge David, Rodríguez-Tirado Areli, Balderas David, López-Caudana Edgar Omar, Moreno-García Carlos Francisco
Tecnologico de Monterrey, School of Engineering and Sciences, Monterrey, United Kingdom.
Robert Gordon University, School of Computing, Aberdeen, United Kingdom.
Front Robot AI. 2022 Apr 4;9:834021. doi: 10.3389/frobt.2022.834021. eCollection 2022.
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.
使用一个或多个机器人进行迷宫导航已成为科学文献和实际生活中的一个反复出现的挑战，机群必须找到更快、更好的方法来在诸如交通枢纽、机场等环境中导航，或用于灾区疏散。人们已经探索了许多方法来解决这个问题，包括各种传感器和其他信号接收系统的应用。最有趣的是，基于摄像头的技术在这类场景中变得越来越受欢迎，因为它们具有鲁棒性和可扩展性。在本文中，我们实施了一种端到端策略来应对这种场景，使机器人能够通过计算机视觉和路径规划以自主方式解决迷宫问题。此外，该机器人通过通信协议将生成的知识共享给另一个机器人，并且必须调整其机械特性以能够应对相同的挑战。本文展示了该解决方案的四个组件的实验验证，即相机校准、迷宫映射、路径规划和机器人通信。最后，我们展示了在一对具有不同机械特性的机器人上进行的一些初步实验。这项工作的进一步实施包括让机器人进行其他任务的通信，如教学辅助、远程课程以及高等教育中的其他创新。
