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18 pages, 3757 KB

Open AccessArticle

Recovery Motion Analysis for False Ceiling Inspection Robot

by Matthew S. K. Yeo, Zhenyuan Yang, S. M. Bhagya P. Samarakoon and R. E. Mohan

Appl. Sci. 2025, 15(9), 4616; https://doi.org/10.3390/app15094616 - 22 Apr 2025

Viewed by 803

The false ceiling plenum is a common and essential part of building infrastructure. However, false ceiling infrastructure requires constant maintenance, which is cumbersome and dangerous for humans since they have to work at high heights and conduct repetitive actions for false ceiling panel replacement. As a solution, robots have been developed to inspect false ceilings. However, these robots can fall during navigation in false ceilings, such as in rugged areas. Therefore, this paper discusses the self-righting capabilities implemented on a false ceiling inspection robot known as FalconX. Mechanisms that aid in self-righting the robot back to a moving position after being toppled due to obstacles within the false ceiling environment were explored, along with their force analysis. Simulations were conducted in Gazebo environments and real hardware experiments were conducted to validate the robot’s self-righting capabilities. The experimental results confirm the self-righting capability of the robot. Full article

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20 pages, 9995 KB

Open AccessArticle

False Ceiling Deterioration Detection and Mapping Using a Deep Learning Framework and the Teleoperated Reconfigurable ‘Falcon’ Robot

by Archana Semwal, Rajesh Elara Mohan, Lee Ming Jun Melvin, Povendhan Palanisamy, Chanthini Baskar, Lim Yi, Sathian Pookkuttath and Balakrishnan Ramalingam

Sensors 2022, 22(1), 262; https://doi.org/10.3390/s22010262 - 30 Dec 2021

Cited by 7 | Viewed by 4149

Abstract

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. Full article

(This article belongs to the Special Issue Advanced Sensors Technologies Applied in Mobile Robot)

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21 pages, 52231 KB

Open AccessArticle

Robot-Inclusive False Ceiling Design Guidelines

by Matthew S. K. Yeo, S. M. Bhagya P. Samarakoon, Qi Boon Ng, Yi Jin Ng, M. A. Viraj J. Muthugala, Mohan Rajesh Elara and Raymond W. W. Yeong

Buildings 2021, 11(12), 600; https://doi.org/10.3390/buildings11120600 - 1 Dec 2021

Cited by 10 | Viewed by 7217

Abstract

False ceilings are often utilised in residential and commercial spaces as a way to contain and conceal necessary but unattractive building infrastructure, including mechanical, electrical, and plumbing services. Concealing such elements has made it difficult to perform periodic inspection safely for maintenance. To complement this, there have been increasing research interests in mobile robots in recent years that are capable of accessing hard-to-reach locations, thus allowing workers to perform inspections remotely. However, current initiatives are met with challenges arising from unstructured site conditions that hamper the robot’s productivity for false ceiling inspection. The paper adopts a top-down approach known as “Design for Robots”, taking into account four robot-inclusive design principles: activity, accessibility, safety, observability. Falcon, a class of inspection robots, was used as a benchmark to identify spatial constraints according to the four principles. Following this, a list of false ceiling design guidelines for each category are proposed. Full article

(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)

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17 pages, 15235 KB

Open AccessArticle

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

by Balakrishnan Ramalingam, Thein Tun, Rajesh Elara Mohan, Braulio Félix Gómez, Ruoxi Cheng, Selvasundari Balakrishnan, Madan Mohan Rayaguru and Abdullah Aamir Hayat

Sensors 2021, 21(16), 5326; https://doi.org/10.3390/s21165326 - 6 Aug 2021

Cited by 9 | Viewed by 4283

Abstract

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. Full article

(This article belongs to the Section Sensors and Robotics)

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18 pages, 4995 KB

Open AccessArticle

Falcon: A False Ceiling Inspection Robot

by M. A. Viraj J. Muthugala, Koppaka Ganesh Sai Apuroop, Saurav Ghante Anantha Padmanabha, S. M. Bhagya P. Samarakoon, Mohan Rajesh Elara and Raymond Yeong Wei Wen

Sensors 2021, 21(16), 5281; https://doi.org/10.3390/s21165281 - 5 Aug 2021

Cited by 8 | Viewed by 3880

Abstract

Frequent inspections are essential for false ceilings to maintain the service infrastructures, such as mechanical, electrical, and plumbing, and the structure of false ceilings. Human-labor-based conventional inspection procedures for false ceilings suffer many shortcomings, including safety concerns. Thus, robot-aided solutions are demanded for false ceiling inspections similar to other building maintenance services. However, less work has been conducted on developing robot-aided solutions for false ceiling inspections. This paper proposes a novel design for a robot intended for false ceiling inspections named Falcon. The compact size and the tracked wheel design of the robot allow it to traverse obstacles such as runners and lighting fixtures. The robot’s ability to autonomously follow the perimeter of a false ceiling can improve the productivity of the inspection process since the heading of the robot often changes due to the nature of the terrain, and continuous heading correction is an overhead for a teleoperator. Therefore, a Perimeter-Following Controller (PFC) based on fuzzy logic was integrated into the robot. Experimental results obtained by deploying a prototype of the robot design to a false ceiling testbed confirmed the effectiveness of the proposed PFC in perimeter following and the robot’s features, such as the ability to traverse on runners and fixtures in a false ceiling. Full article

(This article belongs to the Section Sensors and Robotics)

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20 pages, 35740 KB

Open AccessArticle

Towards an Optimal Footprint Based Area Coverage Strategy for a False-Ceiling Inspection Robot

by Thejus Pathmakumar, Vinu Sivanantham, Saurav Ghante Anantha Padmanabha, Mohan Rajesh Elara and Thein Than Tun

Sensors 2021, 21(15), 5168; https://doi.org/10.3390/s21155168 - 30 Jul 2021

Cited by 10 | Viewed by 2794

Abstract

False-ceiling inspection is a critical factor in pest-control management within a built infrastructure. Conventionally, the false-ceiling inspection is done manually, which is time-consuming and unsafe. A lightweight robot is considered a good solution for automated false-ceiling inspection. However, due to the constraints imposed by less load carrying capacity and brittleness of false ceilings, the inspection robots cannot rely upon heavy batteries, sensors, and computation payloads for enhancing task performance. Hence, the strategy for inspection has to ensure efficiency and best performance. This work presents an optimal functional footprint approach for the robot to maximize the efficiency of an inspection task. With a conventional footprint approach in path planning, complete coverage inspection may become inefficient. In this work, the camera installation parameters are considered as the footprint defining parameters for the false ceiling inspection. An evolutionary algorithm-based multi-objective optimization framework is utilized to derive the optimal robot footprint by minimizing the area missed and path-length taken for the inspection task. The effectiveness of the proposed approach is analyzed using numerical simulations. The results are validated on an in-house developed false-ceiling inspection robot—Raptor—by experiment trials on a false-ceiling test-bed. Full article

(This article belongs to the Section Sensors and Robotics)

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