Dinakaran Devaraj

This paper presents the design of a modular mobile robot capable of climbing and inspecting vertical ferromagnetic pipes using magnetic wheels. Mobile robots used for climbing ferromagnetic surfaces employ magnetic tracks, wheels, and magnets attached to the robot’s body. When it comes to ferromagnetic pipes, magnetic wheels and magnets attached to the body can be used. Among them, magnetic wheels are commonly used for inspecting ferromagnetic pipes. While current robots are suitable for large pipes, they are not practical for smaller ones. To address this gap, a small-sized robot equipped with a magnetic wheel system that ensures both strong attachment and smooth movement along vertical ferromagnetic surfaces is developed. The robot’s magnetic adhesion performance was analyzed through simulations using finite element method magnetics and validated through laboratory experiments. The results show an average error of only 8.25% between simulation and real-world tests, confirming the system’s reliability for external pipe inspection.

This paper presents the design of a modular mobile robot capable of climbing and inspecting vertical ferromagnetic pipes using magnetic wheels. Mobile robots used for climbing ferromagnetic surfaces employ magnetic tracks, wheels, and magnets attached to the robot’s body. When it comes to ferromagnetic pipes, magnetic wheels and magnets attached to the body can be used. Among them, magnetic wheels are commonly used for inspecting ferromagnetic pipes. While current robots are suitable for large pipes, they are not practical for smaller ones. To address this gap, a small-sized robot equipped with a magnetic wheel system that ensures both strong attachment and smooth movement along vertical ferromagnetic surfaces is developed. The robot’s magnetic adhesion performance was analyzed through simulations using finite element method magnetics and validated through laboratory experiments. The results show an average error of only 8.25% between simulation and real-world tests, confirming the system’s reliability for external pipe inspection.

This paper presents the design of a modular mobile robot capable of climbing and inspecting vertical ferromagnetic pipes using magnetic wheels. Mobile robots used for climbing ferromagnetic surfaces employ magnetic tracks, wheels, and magnets attached to the robot’s body. When it comes to ferromagnetic pipes, magnetic wheels and magnets attached to the body can be used. Among them, magnetic wheels are commonly used for inspecting ferromagnetic pipes. While current robots are suitable for large pipes, they are not practical for smaller ones. To address this gap, a small-sized robot equipped with a magnetic wheel system that ensures both strong attachment and smooth movement along vertical ferromagnetic surfaces is developed. The robot’s magnetic adhesion performance was analyzed through simulations using finite element method magnetics and validated through laboratory experiments. The results show an average error of only 8.25% between simulation and real-world tests, confirming the system’s reliability for external pipe inspection.

Recent technological advancements have fostered human–robot coexistence in work and residential environments. The assistive robot must exhibit humane behavior and consistent care to become an integral part of the human habitat. Furthermore, the robot requires an adaptive unsupervised learning model to explore unfamiliar conditions and collaborate seamlessly. This paper introduces variants of the growing hierarchical self-organizing map (GHSOM)-based computational models for assistive robots, which constructs knowledge from unsupervised exploration-based learning. Traditional self-organizing map (SOM) algorithms have shortcomings, including finite neuron structure, user-defined parameters, and non-hierarchical adaptive architecture. The proposed models overcome these limitations and dynamically grow to form problem-dependent hierarchical feature clusters, thereby allowing associative learning and symbol grounding. Infants can learn from their surroundings through exploration and experience, developing new neuronal connections as they learn. They can also apply their prior knowledge to solve unfamiliar problems. With infant-like emergent behavior, the presented models can operate on different problems without modifications, producing new patterns not present in the input vectors and allowing interactive result visualization. The proposed models are applied to the color, handwritten digits clustering, finger identification, and image classification problems to evaluate their adaptiveness and infant-like knowledge building. The results show that the proposed models are the preferred generalized models for assistive robots.

Pipelines are crucial in transportation, requiring regular maintenance to function correctly. Robots perform maintenance works, and each robot moves through the pipes using a different mechanism. The In-Pipe Inspection robots are categorized based on their locomotion types. The mechanisms utilized by wheeled wall-pressed type In-Pipe Inspection Robots are divided into dependent and independent mechanisms. The present wheeled IPIR using the dependent mechanism finds difficulty in passing through vertical pipe with obstacles. In this study, one robot from each mechanism is chosen, and the mobility of that robot inside a vertical pipe with an obstacle is examined through simulation using the MSC ADAMS software and observed experimentally. The results show that the robot wheels used in the independent mechanism always make contact with the pipeline's inner surface compared to the dependent mechanism. This result indicates that the robot with the independent mechanism is more suitable for In-Pipe inspection.

The most popular method for transporting fluids, and gases is through pipelines. For them to work correctly, regular inspection is necessary. Humans must enter potentially dangerous environments to inspect pipelines. As a result, pipeline robots came into existence. These robots aid in pipeline inspection, protecting numerous people from harm. Despite numerous improvements, pipeline robots still have several limitations. This paper presents the design and motion planning of a wheeled type pipeline inspection robot that can inspect pipelines having an inner diameter between 250 mm to 350 mm. The traditional wheeled robot design has three wheels fixed symmetrically at a 120° angle apart from each other. When maneuvering through a curved pipeline, this robot encounters motion singularity. The proposed robot fixes the wheels at different angles to address this issue, allowing the robot to stay in constant contact with the pipe's surface. Motion analysis is done for the proposed and existing robot design to study their behavior inside the pipeline. The result shows that the proposed robot avoids motion singularity and improves mobility inside pipelines. 3d printing technology aids in the development of the proposed robot. The experimental tests on the developed robot inside a 300 mm-diameter straight and curved pipeline show that the robot avoids motion singularity.

This paper presents a modular design of a wheeled wall-press type In-pipe Inspection Robot using 3D printing technology. The proposed robot (Kuzhali) consists of a prismatic joint to which the modular parts are attached. This mechanism ensures that the wheels are in contact with the pipe's internal surface. Finite Element Analysis (FEA) was performed on the custom-designed parts to see whether they can withstand the stress acting on them while passing through straight and curved pipelines. The analysis was carried out for curved pipes with a Factor of Safety of 1.5. To optimize the performance and durability of the robotic legs, the thickness of the components are modified. The design of the components was validated with an error of ± 5 %. The new design was fabricated and it was found that the proposed robot was able to move through straight, curved and L-branch pipes in the horizontal plane.