Singh Ishneet Sukhvinder, Wijegunawardana I D, Samarakoon S M Bhagya P, Muthugala M A Viraj J, Elara Mohan Rajesh
Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore, 487372, Singapore.
Temasek Junior College, Singapore, 469278, Singapore.
Sci Rep. 2023 Aug 6;13(1):12741. doi: 10.1038/s41598-023-38538-3.
Cleaning is a fundamental routine task in human life that is now handed over to leading-edge technologies such as robotics and artificial intelligence. Various floor-cleaning robots have been developed with different cleaning functionalities, such as vacuuming and scrubbing. However, failures can occur when a robot tries to clean an incompatible dirt type. These situations will not only reduce the efficiency of the robot but also impose severe damage to the robots. Therefore, developing effective methods to classify the cleaning tasks performed in different regions and assign them to the respective cleaning agent has become a trending research domain. This article proposes a vision-based system that employs YOLOv5 and DeepSORT algorithms to detect and classify dirt to create a dirt distribution map that indicates the regions to be assigned for different cleaning requirements. This map would be useful for a collaborative cleaning framework for deploying each cleaning robot to its respective region to achieve an uninterrupted and energy-efficient operation. The proposed method can be executed with any mobile robot and on any surface and dirt, achieving high accuracy of 81.0%, for dirt indication in the dirt distribution map.
清洁是人类生活中的一项基本日常任务，如今已交给机器人技术和人工智能等前沿技术。人们已经开发出了各种具有不同清洁功能的扫地机器人，如吸尘和擦洗。然而，当机器人试图清洁不相容的污垢类型时，可能会出现故障。这些情况不仅会降低机器人的效率，还会对机器人造成严重损坏。因此，开发有效的方法来对不同区域执行的清洁任务进行分类，并将其分配给相应的清洁剂，已成为一个热门研究领域。本文提出了一种基于视觉的系统，该系统采用YOLOv5和DeepSORT算法来检测和分类污垢，以创建一个污垢分布图，该图指示了针对不同清洁要求应分配的区域。该地图对于协作清洁框架很有用，该框架可将每个清洁机器人部署到其各自的区域，以实现不间断且节能的操作。所提出的方法可以在任何移动机器人上以及任何表面和污垢上执行，在污垢分布图中污垢指示的准确率高达81.0%。
