Ubuntu system monitor zig zag line processes11/23/2023 ![]() ![]() In contrast with auto cleaning/scheduled cleaning mode, the selective area cleaning/spot cleaning mode could save energy by avoiding non-dirty areas, reducing the cleaning time, and enhancing the lifetime of the robot 6. In selective area cleaning/spot cleaning mode, the robot will clean only dirty areas instead of cleaning the whole region. Here, the auto cleaning/scheduled cleaning mode cleans the given workspace autonomously once a day or at a scheduled time interval using a pre-build floor map. It performs the cleaning task in public premises using two cleaning modes: auto cleaning/scheduled cleaning mode and selective area cleaning/spot cleaning mode 2, 3, 4, 5. In recent years, mobile cleaning robots have been widely used to clean and maintain commercial premises and help overcome the workforce shortage. Due to long working hours, low wages, and unwillingness to work as a cleaner, workforce shortage has been a constant problem for cleaning and maintenance tasks in commercial premises 1, 2. Hence, frequent cleaning is essential in commercial premises in pandemic situations like COVID-19. ![]() Therefore, the chances of accumulating trash, stains (footprint stains and other forms of stains), and debris are quite high, raising safety and hygiene concerns. Generally, human traffic is heavy on commercial premises like shopping malls, food courts, community clubs, and hospitals. Further, compared to conventional methods, the evolutionary-based optimization path planning scheme reduces 15% percent of navigation time and 10% percent of energy consumption. The experimental results show that the SSD MobileNet algorithm scored 90% accuracy for stain and trash detection on the floor. Further, optimal shortest waypoint coverage path planning using evolutionary-based optimization was incorporated to traverse the robot efficiently to the designated selective area cleaning/spot cleaning regions. Here, a deep Simple Online and Real-time Tracking (SORT) human tracking algorithm was used to trace the high human traffic region and Single Shot Detector (SSD) MobileNet object detection framework for detecting the dirty region. The selective area cleaning/spot cleaning region is identified based on the combination of two strategies: tracing the human traffic patterns and detecting stains and trash on the floor. In this scheme, the robot will clean only dirty areas instead of the whole region. This work proposes a novel selective area cleaning/spot cleaning framework for indoor floor cleaning robots using RGB-D vision sensor-based Closed Circuit Television (CCTV) network, deep learning algorithms, and an optimal complete waypoints path planning method. However, frequent cleaning tasks adversely impact the robot’s performance and utilize more cleaning accessories (such as brush, scrubber, and mopping pad). Floor cleaning robots are widely used in public places like food courts, hospitals, and malls to perform frequent cleaning tasks. ![]()
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |