2020· International Journal of Intelligent Automation & Robotics Engineering· Vol 3, pp. 01-18· 0 citations
Abstract
Rapid urbanization, industrialization, and aging infrastructure have increased the need for efficient monitoring systems. Traditional manual inspections of bridges, tunnels, pipelines, dams, railway tracks, and industrial facilities are costly, time-consuming, labor-intensive, and risky. Autonomous inspection robots offer an advanced solution for smart infrastructure monitoring and maintenance. This study reviews autonomous inspection robots developed before February 2019, focusing on their design, navigation, sensors, communication systems, and control methods. These robots use technologies such as LiDAR, ultrasonic sensors, infrared cameras, thermal imaging, GPS, and wireless communication for real-time monitoring, defect detection, and predictive maintenance. Machine learning and computer vision further improve inspection accuracy. Different robot types, including wheeled, tracked, aerial, climbing, underwater, and hybrid robots, are compared based on mobility, adaptability, energy efficiency, and inspection performance. The paper also proposes an autonomous wheeled inspection robot using sensor fusion and computer vision for obstacle avoidance, wireless communication, and autonomous navigation. Results show that autonomous inspection robots improve safety, fault detection, and inspection efficiency compared to manual methods. Challenges such as power consumption, communication delays, localization errors, and sensor calibration are discussed. Future developments involving AI, IoT, cloud robotics, edge computing, swarm robotics, and digital twins are expected to enhance intelligent infrastructure monitoring systems.
This study proposes an AI-enabled autonomous drone framework for infrastructure inspection that integrates intelligent flight planning, automated data collection, computer vision-based defect detection, and condition assessment that enhances inspection accuracy, operational safety, and scalability compared to traditional methods.
Yuki Nakamura· International Journal of Mod...· 0 citations
Intelligent robotic navigation has become an important research area for autonomous systems operating in dynamic and uncertain environments. Before 2019, advancements in robotics, artificial intelligence, embedded systems, and wireless communication improved autonomous robots used in healthcare, agriculture, transportation, military, and service applications. Traditional single-sensor systems faced challenges such as low localization accuracy and poor environmental perception. To address these issues, Hybrid Wireless Sensor Networks (HWSNs) combined sensors like LiDAR, GPS, IMUs, cameras, ultrasonic sensors, and RFID to enhance navigation, obstacle detection, mapping, and reliability. Research focused on sensor fusion, SLAM, Kalman filtering, fuzzy logic, neural networks, machine learning, and real-time obstacle avoidance. The proposed hybrid framework supports accurate localization, autonomous mapping, and efficient path planning for indoor and outdoor environments. Experimental results show that hybrid sensor systems improve navigation accuracy, reduce localization errors, and enhance obstacle detection efficiency. The study concludes that hybrid sensor networks are essential for future autonomous robotic systems, with future research focusing on deep reinforcement learning, cloud robotics, edge computing, cognitive navigation, and IoRT-based architectures.
Hiroshi Tanaka, Yuki Nakamura· International Journal of Int...· 0 citations
Autonomous navigation of robots is a key technology for intelligent transport, industrial automation, and service robotics. This paper reviews its core technical framework and typical applications, focusing on five closely connected modules: perception, localisation and mapping, path planning, obstacle avoidance, and decision-making control. It first explains how vision sensors, LiDAR, millimetre-wave radar, IMU, and other sensing devices support environmental perception across different scenarios, and why multi-sensor fusion is necessary to improve robustness. Then, it discusses major localisation and mapping methods, including SLAM-based approaches, as well as path planning and control algorithms such as A*, DWA, TEB, MPC, and reinforcement learning. The paper further compares the application characteristics of autonomous navigation in intelligent transportation, industrial logistics, and medical service robots, showing that different scenarios require different balances between robustness, efficiency, accuracy, safety, and energy consumption. Finally, it points out future trends, including cloud-edge-device collaboration, end-to-end learning, multimodal large models, embodied intelligence, and cooperative navigation, which may further promote intelligent and sustainable robot mobility.
Pipeline infrastructure carries oil, gas, water, and industrial fluids across vast distances, and require careful maintenance. Visual inspections, scheduled digs, inline gauging tools, etc. have been industry standards for years, but struggle with expensive operations, scheduled downtime, and the inability to provide continuous monitoring. Autonomous robots have the potential to improve pipeline inspection workflows. Some robotic inspection platforms are equipped with advanced locomotion technologies like wheels, tracks, as well as embedded sensor payloads to detect corrosion, cracks, leakage, etc. Optical inspections can be augmented with ultrasonic testing, magnetic flux leakage, thermal imaging, acoustic sensors to provide comprehensive pipeline assessments. AI and machine learning allow for increased automation of anomaly detection and failure prediction. Beyond sensing, data storage and transfer allows for processed information to be used by operational teams and management. Edge computing allows for time-critical processes to be run on-board, while cloud computing allows for data storage and big-data analysis. SCADA integration can connect robotic inspections to enterprise-level risk analysis. Current limitations of pipeline inspection robots include limited energy storage, complex data analysis, deployment in difficult terrains/depths, and a lack of skilled pipeline operators. Some areas of development include energy harvesting robots, swarm robotics for distributed inspection, and multi-modal sensing/data fusion.
Wegner Chukwuemeka Dulo, Jolly-Adjarho Ogheneakpobor· E3S Web of Conferences· 0 citations
The paper provides the design and implementation of sensor-fusion-guided mobile robotic vehicle in efficient and safe industrial material handling. The suggested system is implemented with the help of an Arduino Uno microcontroller with several sensors such as ultrasonic sensors, infrared (IR) sensors and wheel encoders to provide efficient perception and tracking of the environment. The fusion of these heterogeneous sensors is achieved through sensor fusion so that the weaknesses may be overcome, which allows the sensors to detect obstacles reliably, guide paths and navigate successfully in dynamic industrial settings. Ultrasonic sensors offer a range of distance measurements that are long, whereas IR sensors offer a high accuracy of short-range measurements, and the wheel encoders facilitate odometry-based estimation of movement. An ESP8266/ESP32 IoT chip is also installed in the robotic vehicle, which enables real-time tracking, assigning tasks remotely and visualizing the status of the system with the help of a cloud interface. The driver allows motor control, which is the L293D driver, and brings smooth and precise movements of wheels to move through the predetermined industrial paths autonomously. The solution suggested can greatly decrease the number of people on the work, eliminate the risks of collisions, and enhance the efficiency of the working process on the warehouse floor and factory premises. The system combines affordable hardware with smart sensor fusion and IoT connectivity to provide a scalable and cost-effective and reliable solution to modern smart industry-wide material transportation.
V. Vijayabhasker, Beerala Venkatanaryana, Mahesh Mudavath· International journal of com...· 0 citations
Continuous monitoring of the industry setting is necessary to maintain efficiency of operations, safety, and predictive maintenance. The traditional industrial monitoring systems are highly dependent on localized sensors and human observation, which result in expensive installation, restricted coverage zone and could be unsafe to operators. The recent progress in the embedded systems, robotics, and low-cost sensors has made it possible to design the autonomous mobile robot able to conduct the industrial inspection tasks effectively. The present paper introduces the design, development, and testing of the low cost autonomous robot that could be used in the industrial monitoring technology. The suggested system incorporates inexpensive sensing sub-system, control architecture based on microcontrollers, wireless communication and autonomous navigation. Robot has the potential to check environmental conditions like temperature, humidity, gas content and vibration and also move freely within the industrial floors and relay real time statistics to a monitoring station. To be able to scale and be cost-effective, it adopts a modular hardware design and a layered software architecture. There are sensor fusion methods used in obstacle detection and navigation, and power-saving algorithms used in increasing battery life. Through experiment verification it is proved that above the developed robot has a sure monitoring performance with satisfactory accuracy at relatively low cost as compared to other standard industrial monitoring systems. The offered solution can be taken as an alternative to small- and medium-scale industries, which can enhance monitoring, safety, and reliability of operations by automating their tasks. It is rather economical and practical.
S. Verma, Naveen Kumar· International Journal of Mod...· 0 citations
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