sTetro-Deep Learning Powered Staircase Cleaning and Maintenance Reconfigurable Robot

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 94.32% for staircase recognition tasks and 93.81% accuracy for obstacle and debris detection tasks during real operation of the robot.

Object Detection with Hyperparameter and Image Enhancement Optimisation for a Smart and Lean Pick-and-Place Solution

“…In the architecture of SSD MobileNetV2 FPNnetwork, SSD as a detection network, and FPN-Lite that MobileNet V2 has three layers with two kinds block with stride 1 while the second block is a stride The first layer of MobileNet V2 is a 1 × 1 convoluti with an upper cut-off at 6 or ReLU6, the second lay and the third layer is a 1 × 1 convolution layer witho Many studies have already been conducted on work hyperparameter tuning to increase model accu V2 FPNLite is being implemented in various applic [8], traffic density [9], vulnerable road users [10], and [11]. For robotic automation, SSD320 Mobilenet V2 such as agriculture [12,13] and hospitality [14,15].…”

“…Increasingly, SSD320 Mobilenet V2 FPNLite is being implemented in various applications such as road damage detection [8], traffic density [9], vulnerable road users [10], and real-time road-based object detection [11]. For robotic automation, SSD320 Mobilenet V2 FPNLite is used in various industries such as agriculture [12,13] and hospitality [14,15].…”

Towards a Low-Cost Object Collecting and Organizing Household Robot using Deep Learning

“…One of the most popular is the cleaning robot, the Roomba vacuum, which automatically cleans the dust from the carpet and the floor avoiding obstacles [5]. The work in [37] developed a staircase-cleaning robot with object detection using deep learning. However, the robots in [5] and [37] do not have any hands and they can't pick up any large object from the floor and can't put the object in the proper place.…”

“…The work in [37] developed a staircase-cleaning robot with object detection using deep learning. However, the robots in [5] and [37] do not have any hands and they can't pick up any large object from the floor and can't put the object in the proper place. In [6], an algorithm is implemented in a PR2 robot [7] for folding laundry.…”

Drain Structural Defect Detection and Mapping Using AI-Enabled Reconfigurable Robot Raptor and IoRT Framework

“…Reconfigurable robots are more flexible than fixed-size robots. In recent years, reconfigurable morphology-based robots have been widely used for various automation applications including cleaning indoor and outdoor environments [25,26], inspecting built environments [23,27], ship hull inspection, aircraft structural inspection [28], etc. From the literature, it is realized that designing a reconfigurable morphology-based robot is an optimal solution to overcoming the shortcomings mentioned earlier.…”