IAES International Journal of Robotics and Automation (IJRA)
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Design and implementation of Internet of Things-enabled long-range autonomous surveillance bot for LPG leak detection and environmental safety monitoring
Get PDFLiquefied petroleum gas (LPG) accidents pose significant safety risks, requiring continuous monitoring and Internet of Things (IoT) technology to prevent gas leakage and ensure human safety. This work proposes distributed field-oriented IoT gas sensing robots for detecting dangerous flammable gases like Ammonia, Sulphur Dioxide, Nitrogen Dioxide, and Carbon Dioxide. The SnoLURk solution enables cost-effective IoT gas leak detection in indoor and outdoor robots using budget-friendly casings and sensors. The study also discusses a robotic system for gas leak detection, aiming to detect and combat burglary using ZigBee and GSM modules. Cloud support allows Wi-Fi zone residents to receive alerts and send investigators via email, enabling remote data analytics monitoring. The IoT-based Worker's Health Monitoring System improves health and safety practices in industrial environments by monitoring workers' health 24/7. It allows on-site and off-site monitoring, enabling quick intervention and avoiding complications. The system's applications include construction, mining, manufacturing, and healthcare. Future versions may include improved sensors and machine learning
Edge-aware distilled segmentation with pseudo-label refinement for autonomous driving perception
Get PDFAchieving precise semantic segmentation is essential for enabling real-time perception in autonomous systems, yet leading approaches typically require substantial annotated data and powerful hardware, restricting their use on devices with limited resources. This work introduces an efficient segmentation framework that integrates pseudo-label refinement, knowledge distillation, and entropy-based confidence filtering to train compact student networks suitable for edge deployment. High-quality pseudo-labels are first produced by a robust teacher network, then further improved using a dense conditional random field to boost spatial consistency. An entropy-based selection mechanism removes unreliable predictions, ensuring that only the most trustworthy labels guide the student model's training. The use of knowledge distillation effectively transfers detailed semantic understanding from the teacher to the student, enhancing accuracy without added computational overhead. Experimental results with multiple EfficientNet backbones reveal that this pipeline improves segmentation accuracy and output clarity, while also supporting real-time or near real-time inference on CPUs with limited processing power. Extensive ablation and qualitative studies further confirm the method's robustness and flexibility for real-world edge applications
Antecedents and consequences of memorable experience in the airline industry: service robots versus human staff
Get PDFThe study aims to examine the type of service providers, such as service robots and human staff, as a potential moderator in the relationship between SERVQUAL and memorable experience in the airport industry. In order to verify 15 hypotheses, data were collected from 313 travelers who acquired information from service robots and 313 travelers who acquired information from human staff at the airport. The results of data analysis revealed that the five sub-dimensions of SERVQUAL, including tangibles, reliability, responsiveness, assurance, and empathy, enhance memorable experience. In addition, a memorable experience has a positive effect on customer satisfaction, which subsequently influences attitude and intention to use. In addition, the type of service providers moderated the links between i) responsiveness and memorable experience and ii) empathy and memorable experience
Design and development of a modular magnetic wheeled robot for out-pipe inspection
Get PDFThis 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
NAPLAM: a novel ledger-based algorithm for detection and mitigation of sinkhole attacks in routing protocol for low power and lossy networks-based Internet of things
Get PDFThe Internet of Things (IoT) is a network of connected physical objects that collect and share data over the Internet. However, routing attacks can disrupt data exchange, especially multi-node sinkhole attacks in low power and lossy IoT networks (LLNs). To support communication in LLN IoT, the IPv6-based routing protocol for LLNs (RPL) is used. Despite having several advantages, RPL also faces challenges like being vulnerable to attacks, having limited resources, compatibility, and scalability issues. Additionally, traditional security methods often do not work well for LLN-IoT devices because they lack the necessary computing power. To overcome these challenges, we have proposed a novel ledger-based framework called network and packet ledger to ascertain malicious devices using routing protocol for LLN (NAPLAM-RPL). This framework can effectively detect and mitigate multi-node sinkhole attacks in IoT networks. This paper also compares NAPLAM-RPL with similar protocols using the NetSim Simulator. The experimental analysis shows that NAPLAM-RPL improves network performance and outperforms existing methods like RF-trust, SoS-RPL, INTI, C-TRUST, and heartbeat algorithm in crucial areas, including packet delivery rate (PDR), throughput, End-to-End (E2E) delay, energy consumed, and detection accuracy
ADC-LIO: A direct LiDAR-inertial odometry method based on adaptive distortion covariance
Get PDFFocusing on the localization challenges for robots in dynamic navigation environments, this study proposes a direct LiDAR-inertial odometry (LIO) system named ADC-LIO, which achieves robust pose estimation and accurate map reconstruction using adaptive distortion covariance. ADC-LIO is engineered to address uncertain motion patterns in autonomous mobile robots, effectively integrating LiDAR scan undistortion within the Kalman filtering update process by embedding an iterative smoothing process and a backpropagation strategy. The ADC-LIO architecture enhances point cloud accuracy, improving the system's overall performance and robustness. In addition, an adaptive covariance processing method is developed to resolve motion-induced sensing uncertainties, which calculates different covariances according to the error characteristics of the point cloud. This method enhances the constraints of high-quality point clouds, reduces the limitations on low-quality point clouds, and utilizes information more effectively. Experiments on the publicly available NTU-VIRAL dataset validate the effectiveness of ADC-LIO, which improves pose estimation accuracy and reduces absolute position errors compared to other state-of-the-art methods, including FAST-LIO, Faster-LIO, FR-LIO, and Point-LIO. The proposed ADC-LIO is an appealing odometry method that delivers accurate, real-time, and reliable tracking and map-building results, posing a practical solution for robotic applications in structured indoor and GPS-denied outdoor environments
SCADA system in water storage tanks with NI vision LabVIEW
Get PDFAdvances in technology have driven the need for efficient water management systems. This study presents a SCADA-based water management system that integrates LabVIEW and Arduino to monitor and regulate water levels and flow rates in a storage tank. The system uses an HC-SRF04 ultrasonic sensor for water level measurement with 99.77% accuracy and an HX710 pressure sensor, which achieves 98.54% accuracy. The LabVIEW interface displays real-time data, giving users an intuitive view of system performance. A proportional integral derivative (PID) algorithm optimizes the water pump through pulse width modulation (PWM), achieving water flow rate control. The Ziegler-Nichols method tunes the PID parameters to Kp = 16.59, Ti = 1.102, and Td = 0.2755. This tuning ensures the system maintains a consistent target flow rate of 4 liters per minute (L/min) with minimal variation. Initial testing showed a 2.5% overshoot but stabilized at the desired flow rate within 10 seconds, indicating effective control. This SCADA system reduces water and energy waste by enabling continuous real-time monitoring and control. The system provides accurate data through a LabVIEW interface, ensuring effective and informed operational decisions. This robust solution supports efficient water management for industrial and environmental applications, contributing to sustainability and resource optimization
Position tracking of DC motor with PID controller utilizing particle swarm optimization algorithm with Lévy flight and Doppler effect
Get PDFThis paper presents the implementation of the particle swarm optimization with the Lévy flight Doppler effect (PSO-LFDE) algorithm for optimizing proportional-integral-derivative (PID) controller parameters in a direct current (DC) motor system. Traditional optimization algorithms like particle swarm optimization, whale optimization algorithm, grey wolf optimizer, and moth flame optimization often face challenges in balancing exploration and exploitation, leading to suboptimal performance. The proposed PSO-LFDE algorithm addresses these issues by incorporating Lévy flight for enhanced exploration and the Doppler effect for refined exploitation. The algorithm is validated using MATLAB/Simulink for position control in a DC motor system with step inputs of 10, 30, and 60 cm. Key performance metrics, including rise time, settling time, peak time, and steady-state error, were compared against other optimization methods. PSO-LFDE demonstrated superior performance, achieving a 41.63% improvement in rise time and a 70.20% reduction in peak time compared to other methods. These results highlight PSO-LFDE's effectiveness in optimizing PID controller parameters and improving the dynamic response of DC motor systems, offering a robust solution for real-world control applications
Deep-feed: An Internet of things-enabled smart feeding system for pets powered by deep learning
Get PDFInternet of things (IoT) encompasses a variety of connected devices and technologies designed to improve care, monitoring, and management of pets. IoT technology enables voice or app-driven control of these feeders, allowing pet guardians to remotely dispense food to their pets anytime. In this paper, a novel deep-feed network has been proposed that combines Image and sensor data classification. The inputs, such as camera (images) and sensor data, are sent to the preprocessing stages, where images are preprocessed using a Bilateral filter, and the data using preprocessing techniques such as tokenization, lemmatization, etc. The preprocessed images are sent to the neural network, like a convolutional neural network (CNN) for image classification and a bidirectional gated recurrent unit (BiGRU) to predict the dog's behavior. Next, these two networks are fused, and the fuzzy concept identifies whether the dogs are near the food or not in a cage. If the dog is near the food cage, the control unit will allocate the food and water through the water pump in the dog cage. Then the control unit gives the order to fill the food and water pumps and alerts the user to identify the food in a cage via the Blynk application. The accuracy of the suggested method can reach 99.95%, compared to 84.9%, 87.58%, and 93.91% for conventional models like the cat's monitoring and feeding systems via IoT (CMFSVI), petification, and global system for mobile communications/general packet radio service (GSM/GPRS). In comparison to the current approaches, the accuracy of the suggested methodology increased by 16.09%, 13.8%, and 3.75%, for existing models like CMFSVI, petification, and GSM/GPRS, respectively
Energy efficient clustering and routing method for Internet of Things
Get PDFThe Internet of Things is crucial in monitoring environmental conditions in remote areas, but it faces significant challenges related to energy consumption, which affects network longevity and coverage. Clustering has proven effective in prolonging the life of sensor networks. Adaptive clustering in wireless sensor networks allows for more effective cluster organization via real-time rearranging of sensor nodes according to important parameters, which include energy levels and the distance between them. Fruit fly algorithm (FFA) and ant colony optimization (ACO) are emerging as encouraging techniques for creating clusters and establishing paths, respectively. This paper describes the use of the FFA to make the clustering process better by selecting the best cluster head and reducing energy consumption. This paper proposes a novel solution that integrates ACO for establishing paths with FFA for clustering. This method is tested in both homogeneous and heterogeneous settings using MATLAB, comparing its performance with two existing algorithms: low energy adaptive clustering hierarchy (LEACH) and biogeography-based optimization algorithm (BOA). According to the findings, the suggested algorithm performs noticeably better than BOA and LEACH in the context of coverage area and network service period, especially in heterogeneous settings