Abstract
Staircase cleaning is a crucial and time-consuming task for maintenance of multistory apartments and commercial buildings. There are many commercially available autonomous cleaning robots in the market for building maintenance, but few of them are designed for staircase cleaning. A key challenge for automating staircase cleaning robots involves the design of Environmental Perception Systems (EPS), which assist the robot in determining and navigating staircases. This system also recognizes obstacles and debris for safe navigation and efficient cleaning while climbing the staircase. This work proposes an operational framework leveraging the vision based EPS for the modular re-configurable maintenance robot, called sTetro. The proposed system uses an SSD MobileNet real-time object detection model to recognize staircases, obstacles and debris. Furthermore, the model filters out false detection of staircases by fusion of depth information through the use of a MobileNet and SVM. The system uses a contour detection algorithm to localize the first step of the staircase and depth clustering scheme for obstacle and debris localization. The framework has been deployed on the sTetro robot using the Jetson Nano hardware from NVIDIA and tested with multistory staircases. The experimental results show that the entire framework takes an average of 310 ms to run and achieves an accuracy of for staircase recognition tasks and accuracy for obstacle and debris detection tasks during real operation of the robot.
Highlights
With the advent of the 21st century, autonomous robots have become an integral part of intelligent machines for performing various tasks including inspection, manufacturing, warehouse handling, and maintenance
This work proposes an operation framework empowered by the deep learning based environmental perception system for our modular re-configurable staircase cleaning robot sTetro [3,13]
We provide two Time Of Flight distance sensors (TOF) mounted on the front face of the first block, and two mechanical limit switches assembled in front of the second and third blocks
Summary
With the advent of the 21st century, autonomous robots have become an integral part of intelligent machines for performing various tasks including inspection, manufacturing, warehouse handling, and maintenance (cleaning, dusting and painting etc.). In [3,13], the authors propose a modular re-configurable robot sTetro for staircase cleaning where the robot is equipped with a vertical conveyor mechanism for climbing the staircase These robots use an array of sensors such as 2D LiDAR and 1D laser sensors [3,13] and Position Sensitive Detectors (PSDs) [10,11] to recognize their environment. This work proposes an operation framework empowered by the deep learning based environmental perception system for our modular re-configurable staircase cleaning robot sTetro [3,13]. The processing unit utilizes Rosserial communication interface to enable the communication between ROS nodes, and other modules include environmental perception unit and Arduino control blocks This bridge is used to communicate the sensor data and trajectory information between Arduino Mega microcontroller and Jetson Nano unit. Side sweepers spin inwards to grab the dust particle and push towards the suction mouth; the suction unit collects to the collection chamber
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