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1.
Sci Rep ; 11(1): 22378, 2021 Nov 17.
Artigo em Inglês | MEDLINE | ID: mdl-34789747

RESUMO

Drain blockage is a crucial problem in the urban environment. It heavily affects the ecosystem and human health. Hence, routine drain inspection is essential for urban environment. Manual drain inspection is a tedious task and prone to accidents and water-borne diseases. This work presents a drain inspection framework using convolutional neural network (CNN) based object detection algorithm and in house developed reconfigurable teleoperated robot called 'Raptor'. The CNN based object detection model was trained using a transfer learning scheme with our custom drain-blocking objects data-set. The efficiency of the trained CNN algorithm and drain inspection robot Raptor was evaluated through various real-time drain inspection field trial. The experimental results indicate that our trained object detection algorithm has detect and classified the drain blocking objects with 91.42% accuracy for both offline and online test images and is able to process 18 frames per second (FPS). Further, the maneuverability of the robot was evaluated from various open and closed drain environment. The field trial results ensure that the robot maneuverability was stable, and its mapping and localization is also accurate in a complex drain environment.

2.
Sensors (Basel) ; 21(21)2021 Nov 01.
Artigo em Inglês | MEDLINE | ID: mdl-34770593

RESUMO

Human visual inspection of drains is laborious, time-consuming, and prone to accidents. This work presents an AI-enabled robot-assisted remote drain inspection and mapping framework using our in-house developed reconfigurable robot Raptor. The four-layer IoRT serves as a bridge between the users and the robots, through which seamless information sharing takes place. The Faster RCNN ResNet50, Faster RCNN ResNet101, and Faster RCNN Inception-ResNet-v2 deep learning frameworks were trained using a transfer learning scheme with six typical concrete defect classes and deployed in an IoRT framework remote defect detection task. The efficiency of the trained CNN algorithm and drain inspection robot Raptor was evaluated through various real-time drain inspection field trials using the SLAM technique. The experimental results indicate that robot's maneuverability was stable, and its mapping and localization were also accurate in different drain types. Finally, for effective drain maintenance, the SLAM-based defect map was generated by fusing defect detection results in the lidar-SLAM map.


Assuntos
Aves Predatórias , Robótica , Algoritmos , Animais , Humanos
3.
Sensors (Basel) ; 21(16)2021 Aug 06.
Artigo em Inglês | MEDLINE | ID: mdl-34450767

RESUMO

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.


Assuntos
Redes Neurais de Computação , Roedores , Algoritmos , Animais , Ratos
4.
Sensors (Basel) ; 22(1)2021 Dec 30.
Artigo em Inglês | MEDLINE | ID: mdl-35009802

RESUMO

Periodic inspection of false ceilings is mandatory to ensure building and human safety. Generally, false ceiling inspection includes identifying structural defects, degradation in Heating, Ventilation, and Air Conditioning (HVAC) systems, electrical wire damage, and pest infestation. Human-assisted false ceiling inspection is a laborious and risky task. This work presents a false ceiling deterioration detection and mapping framework using a deep-neural-network-based object detection algorithm and the teleoperated 'Falcon' robot. The object detection algorithm was trained with our custom false ceiling deterioration image dataset composed of four classes: structural defects (spalling, cracks, pitted surfaces, and water damage), degradation in HVAC systems (corrosion, molding, and pipe damage), electrical damage (frayed wires), and infestation (termites and rodents). The efficiency of the trained CNN algorithm and deterioration mapping was evaluated through various experiments and real-time field trials. The experimental results indicate that the deterioration detection and mapping results were accurate in a real false-ceiling environment and achieved an 89.53% detection accuracy.

5.
Sensors (Basel) ; 20(18)2020 Sep 15.
Artigo em Inglês | MEDLINE | ID: mdl-32942750

RESUMO

Insect detection and control at an early stage are essential to the built environment (human-made physical spaces such as homes, hotels, camps, hospitals, parks, pavement, food industries, etc.) and agriculture fields. Currently, such insect control measures are manual, tedious, unsafe, and time-consuming labor dependent tasks. With the recent advancements in Artificial Intelligence (AI) and the Internet of things (IoT), several maintenance tasks can be automated, which significantly improves productivity and safety. This work proposes a real-time remote insect trap monitoring system and insect detection method using IoT and Deep Learning (DL) frameworks. The remote trap monitoring system framework is constructed using IoT and the Faster RCNN (Region-based Convolutional Neural Networks) Residual neural Networks 50 (ResNet50) unified object detection framework. The Faster RCNN ResNet 50 object detection framework was trained with built environment insects and farm field insect images and deployed in IoT. The proposed system was tested in real-time using four-layer IoT with built environment insects image captured through sticky trap sheets. Further, farm field insects were tested through a separate insect image database. The experimental results proved that the proposed system could automatically identify the built environment insects and farm field insects with an average of 94% accuracy.


Assuntos
Aprendizado Profundo , Insetos , Internet das Coisas , Controle de Pragas , Animais , Redes Neurais de Computação
6.
Sensors (Basel) ; 20(6)2020 Mar 18.
Artigo em Inglês | MEDLINE | ID: mdl-32197483

RESUMO

This work presents a table cleaning and inspection method using a Human Support Robot (HSR) which can operate in a typical food court setting. The HSR is able to perform a cleanliness inspection and also clean the food litter on the table by implementing a deep learning technique and planner framework. A lightweight Deep Convolutional Neural Network (DCNN) has been proposed to recognize the food litter on top of the table. In addition, the planner framework was proposed to HSR for accomplishing the table cleaning task which generates the cleaning path according to the detection of food litter and then the cleaning action is carried out. The effectiveness of the food litter detection module is verified with the cleanliness inspection task using Toyota HSR, and its detection results are verified with standard quality metrics. The experimental results show that the food litter detection module achieves an average of 96 % detection accuracy, which is more suitable for deploying the HSR robots for performing the cleanliness inspection and also helps to select the different cleaning modes. Further, the planner part has been tested through the table cleaning tasks. The experimental results show that the planner generated the cleaning path in real time and its generated path is optimal which reduces the cleaning time by grouping based cleaning action for removing the food litters from the table.


Assuntos
Algoritmos , Aprendizado Profundo , Redes Neurais de Computação , Robótica/instrumentação , Saneamento/instrumentação , Alimentos , Humanos , Processamento de Imagem Assistida por Computador , Decoração de Interiores e Mobiliário/instrumentação , Limite de Detecção , Robótica/métodos , Equipamentos de Autoajuda , Carga de Trabalho
7.
Sensors (Basel) ; 20(2)2020 Jan 13.
Artigo em Inglês | MEDLINE | ID: mdl-31941127

RESUMO

Tiling robots with fixed morphology face major challenges in terms of covering the cleaning area and generating the optimal trajectory during navigation. Developing a self-reconfigurable autonomous robot is a probable solution to these issues, as it adapts various forms and accesses narrow spaces during navigation. The total navigation energy includes the energy expenditure during locomotion and the shape-shifting of the platform. Thus, during motion planning, the optimal navigation sequence of a self-reconfigurable robot must include the components of the navigation energy and the area coverage. This paper addresses the framework to generate an optimal navigation path for reconfigurable cleaning robots made of tetriamonds. During formulation, the cleaning environment is filled with various tiling patterns of the tetriamond-based robot, and each tiling pattern is addressed by a waypoint. The objective is to minimize the amount of shape-shifting needed to fill the workspace. The energy cost function is formulated based on the travel distance between waypoints, which considers the platform locomotion inside the workspace. The objective function is optimized based on evolutionary algorithms such as the genetic algorithm (GA) and ant colony optimization (ACO) of the traveling salesman problem (TSP) and estimates the shortest path that connects all waypoints. The proposed path planning technique can be extended to other polyamond-based reconfigurable robots.

8.
Front Neurorobot ; 13: 78, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31616275

RESUMO

Modern engineering problems require solutions with multiple functionalities in order to meet their practical needs to handle a variety of applications in different scenarios. Conventional design paradigms for single design purpose may not be able to satisfy this requirement efficiently. This paper proposes a novel system-of-systems bio-inspired design method framed in a solution-driven bio-inspired design paradigm. The whole design process consists of eight steps, that is, (1) biological solutions identification, (2) biological solutions definition/champion biological solutions, (3) principle extraction from each champion biological solution, (4) merging of extracted principles, (5) solution reframing, (6) problem search, (7) problem definition, and (8) principles application & implementation. The steps are elaborated and a case study of reconfigurable robots is presented following these eight steps. The design originates from the multimodal locomotion capabilities of two species (i.e., spiders and primates) and is analyzed based on the Pugh analysis. The resulting robotic platform could be potentially used for urban patrolling purposes.

9.
Biomed Eng Lett ; 9(2): 153-168, 2019 May.
Artigo em Inglês | MEDLINE | ID: mdl-31168421

RESUMO

The paper aims to provide a state-of-the-art review of methods for evaluating the effectiveness and effect of unloader knee braces on the knee joint and discuss their limitations and future directions. Unloader braces are prescribed as a non-pharmacological conservative treatment option for patients with medial knee osteoarthritis to provide relief in terms of pain reduction, returning to regular physical activities, and enhancing the quality of life. Methods used to evaluate and monitor the effectiveness of these devices on patients' health are categorized into three broad categories (perception-, biochemical-, and morphology-based), depending upon the process and tools used. The main focus of these methods is on the short-term clinical outcome (pain or unloading efficiency). There is a significant technical, research, and clinical literature gap in understanding the short- and long-term consequences of these braces on the tissues in the knee joint, including the cartilage and ligaments. Future research directions may complement existing methods with advanced quantitative imaging (morphological, biochemical, and molecular) and numerical simulation are discussed as they offer potential in assessing long-term and post-bracing effects on the knee joint.

10.
Front Robot AI ; 6: 3, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-33501020

RESUMO

Games and toys have been serving as entertainment tools to humans for a long period of time. While except for entertainment, they can also trigger inspiration and enhance productivity in many other domains such as healthcare and general workplaces. The concept of the game is referred to a series of structured procedures (e.g., card games) and virtual programs. The entertainment artifacts could be a toy or even a handicraft, such as origami and kirigami, for entertainment purposes in a broader sense. Recently, the design of robots and relevant applications in robotics has been emerging in taking inspiration from Games and Entertainment Artifacts (GEA). However, there is a lack of systematic and general process for implementing a GEA-inspired design for developing robot-related applications. In this article, we put forward a design paradigm based on the inspiration of game and entertainment artifacts which is a systematic design approach. The design paradigm could follow two different processes which are driven by problems and solutions, respectively, using analogies of games and entertainment artifacts to build robotic solutions for solving real problems. The problem-driven process starts with an existing real-world problem, which follows the sequences of robotics problem search, robotics problem identification, GEA solution search, GEA solution identification, GEA principle extraction, and the principle implementation. Reversely, the solution-driven process follows the sequence of GEA solution search, GEA solution identification, GEA principle extraction, robotics problem search, robotics problem identification, and principle implementation. We demonstrate the application of the design paradigm using the case study of a new type of reconfigurable floor cleaning robot and its path planning algorithm.

11.
Sensors (Basel) ; 18(8)2018 Aug 07.
Artigo em Inglês | MEDLINE | ID: mdl-30087274

RESUMO

Advancing an efficient coverage path planning in robots set up for application such as cleaning, painting and mining are becoming more crucial. Such drive in the coverage path planning field proposes numerous techniques over the past few decades. However, the proposed approaches were only applied and tested with a fixed morphological robot in which the coverage performance was significantly degraded in a complex environment. To this end, an A-star based zigzag global planner for a novel self-reconfigurable Tetris inspired cleaning robot (hTetro) presented in this paper. Unlike the traditional A-star algorithm, the presented approach can generate waypoints in order to cover the narrow spaces while assuming appropriate morphology of the hTtero robot with the objective of maximizing the coverage area. We validated the efficiency of the proposed planning approach in the Robot Operation System (ROS) Based simulated environment and tested with the hTetro robot in real-time under the controlled scenarios. Our experiments demonstrate the efficiency of the proposed coverage path planning approach resulting in superior area coverage performance in all considered experimental scenarios.

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