Deep Learning Based Pavement Inspection Using Self-Reconfigurable Robot

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Cleaning is an important factor in most aspects of our day-to-day life. This research work brings a solution to the fundamental question of “How clean is clean” by introducing a novel framework for auditing the cleanliness of built infrastructure using mobile robots. The proposed system presents a strategy for assessing the quality of cleaning in a given area and a novel exploration strategy that facilitates the auditing in a given location by a mobile robot. An audit sensor that works by the “touch and inspect” analogy that assigns an audit score corresponds to its area of inspection has been developed. A vision-based dirt-probability-driven exploration is proposed to empower a mobile robot with an audit sensor on-board to perform auditing tasks effectively. The quality of cleaning is quantified using a dirt density map representing location-wise audit scores, dirt distribution pattern obtained by kernel density estimation, and cleaning benchmark score representing the extent of cleanliness. The framework is realized in an in-house developed audit robot to perform the cleaning audit in indoor and semi-outdoor environments. The proposed method is validated by experiment trials to estimate the cleanliness in five different locations using the developed audit sensor and dirt-probability-driven exploration.

Section: Introductionmentioning

confidence: 99%

An Autonomous Robot-Aided Auditing Scheme for Floor Cleaning

Pathmakumar

Kalimuthu

Elara

et al. 2021

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“…Robot-assisted inspection has become more popular in the last decade. It is widely used for various inspection applications, including monitoring built environment defects, power transmission line fault detection, ship hull inspection, industry production management, cleaning applications, and so on, as found in the robotics literature [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. Automating the rodent inspection of the false ceiling using robot-assisted technology is an attractive solution.…”

Section: Introductionmentioning

confidence: 99%

AI Enabled IoRT Framework for Rodent Activity Monitoring in a False Ceiling Environment

Ramalingam

Tun²,

Mohan

et al. 2021

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Routine rodent inspection is essential to curbing rat-borne diseases and infrastructure damages within the built environment. Rodents find false ceilings to be a perfect spot to seek shelter and construct their habitats. However, a manual false ceiling inspection for rodents is laborious and risky. This work presents an AI-enabled IoRT framework for rodent activity monitoring inside a false ceiling using an in-house developed robot called "Falcon." The IoRT serves as a bridge between the users and the robots, through which seamless information sharing takes place. The shared images by the robots are inspected through a Faster RCNN ResNet 101 object detection algorithm, which is used to automatically detect the signs of rodent inside a false ceiling. The efficiency of the rodent activity detection algorithm was tested in a real-world false ceiling environment, and detection accuracy was evaluated with the standard performance metrics. The experimental results indicate that the algorithm detects rodent signs and 3D-printed rodents with a good confidence level.

“…Instead of traditional computer vision based techniques, learning based environmental perception (e.g., Deep Learning) is a new paradigm. The deep learning neural network layers can learn features to distinguish and determine objects at high accuracy and can be used in various vision tasks such as outdoor environmental perception for autonomous vehicles [ 18 , 19 , 20 ], cleaning and maintenance robot vision pipeline [ 2 , 21 , 22 , 23 , 24 ], mobile robot place recognition and mapping tasks [ 25 , 26 , 27 , 28 ]. While these state-of-the-art deep learning algorithms have achieved impressive results for environment perception design, there are some shortcomings such as false detection, which can be due to objects with similar features.…”

Section: Introductionmentioning

confidence: 99%

sTetro-Deep Learning Powered Staircase Cleaning and Maintenance Reconfigurable Robot

Ramalingam

Mohan

Balakrishnan

et al. 2021

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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.