Employing Robotics and Deep Learning in Underground Leak Detection

Awwad, Ameen; Albasha, Lutfi; Mir, Hasan; Mortula, Maruf

doi:10.1109/jsen.2023.3248142

Cited by 10 publications

(4 citation statements)

References 30 publications

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“…MLX90614 is a versatile infrared sensor that can be used for high-precision temperature monitoring. Awwad et al [14] used MLX90614 to track the surface temperature above a leaking pipeline and recorded the corresponding position for each reading, achieving accurate monitoring of leaks based on their thermal anomalies. Rinanto et al [15] improved the recognition algorithm based on MLX90614 hardware for precise measurement of human body surface temperature.…”

Section: Introductionmentioning

confidence: 99%

CCICR-V1.0: An Embeded system for recording Chinese Cuisine Cooker data

Zhang,

Wang,

Bao

et al. 2024

Preprint

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The Chinese cooking process is complex and involves a wide variety of dishes, contains a large amount of data, which poses a great challenge to the intelligentization of cooking. To address this issue, this paper proposes a versatile, highly reliable, user-friendly, and intelligent data recorder for Chinese cuisine (Chinese Cuisine Intelligent Cooker Recorder-V1.0, CCICR-V1.0), which is designed to record the associated data generated during Chinese cooking and provide data support for intelligent cooking robot systems.CCICR-V1.0 is equipped with an STM32 microcontroller as the control core and integrated with temperature, pressure, and attitude sensors, enabling intelligent collection of data related to dishes, ingredients, and cookware movement during the cooking process. For high-frequency and multi-channel data acquisition tasks, CCICR-V1.0 adopts a multi-task data buffering storage method, effectively allocating CPU resources, and utilizes NAND Flash as the storage medium to ensure secure storage, management, and preservation of data. Through testing, CCICR-V1.0 has demonstrated its capability to achieve multi-channel, high-frequency data acquisition and storage in complex environments, resulting in an automation rate increase of 89.9%. The weighing module of CCICR-V1.0 exhibits a maximum relative error of 0.288%, while the non-contact infrared temperature measurement module shows a maximum absolute error and maximum relative error of only 0.65°C and 0.78%, respectively. The non-contact infrared temperature measurement module also achieves a cost reduction of 91.7% and an accuracy improvement of approximately 40%. CCICR-V1.0 effectively captures Chinese cooking data and possesses significant commercial value for widespread adoption and application.

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Section: Introductionmentioning

confidence: 99%

CCICR-V1.0: An Embeded system for recording Chinese Cuisine Cooker data

Zhang,

Wang,

Bao

et al. 2024

Preprint

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“…Artificial intelligence (AI) [1][2][3][4][5][6][7][8] and robots [9][10][11][12][13][14][15][16][17][18][19][20] have given birth to a new wave of technological break throughs whereby the fusion of control systems with cognitive capabilities is not merely a dream but a reality that is not only being improved but also exploited with passion. The convergence is underlined by the Unscented Kalman Filter (UKF) [21][22][23][24][25][26][27][28][29][30][31][32][33], a sophisticated algorithm that has done a lot in advancing the abilities of AI-based robotics [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48].…”

Section: Introductionmentioning

confidence: 99%

The convergence of control and cognition: a bibliometric overview of UKF in AI-infused robotics

Obaideen,

AlShabi,

Bettayeb

et al. 2024

Disruptive Technologies in Information Sciences VIII

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This paper gives a bibliometric summary of Unscented Kalman Filter (UKF) in AI-infused robotics, highlighting its role in unifying control and cognition. Using a systematic approach that includes literature collection from IEEE Xplore, Web of Science and Google Scholar, rigorous screening and selection, and VOSviewer for a comprehensive bibliometric analysis. This analysis reports major trends, primary contributors and central themes, highlighting UKF's pivotal role in improving robotics cognitive and control capacities. The study emphasizes the universally used UKF in many fields of robotics, i.e. in navigation and mapping, sensor fusion, and state estimation, as one of its principal developers, which illustrates its vital role in promoting robotic autonomy and intelligence. The integration of findings from the bibliometric analysis thus not only presents the current state of research but also identifies possible future research directions, highlighting the increasing unification of control theories and cognitive processes in robotics. This research adds to the body of knowledge by delivering a comprehensive map of the UKF application. In this light, the UKF will be able to penetrate AI-infused robotics, the future of robotic developments will rely on the deep fusion of control and cognition facilitated by UKF and alike.

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“…The dynamics of the autonomous navigation landscape change and the effectiveness and robustness of path planning algorithms are critical for the operational success of Unmanned Ground Vehicles (UGVs) [1][2][3][4][5][6][7][8][9][10][11][12]. These vehicles are used in various applications including military operations [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30], disaster reliefs, as well as agricultural automation and require sophisticated navigation techniques that can allow them to navigate safely and effectively through difficult environments [31][32][33][34][35][36][37][38][39][40][41][42][43][44][45].…”

Section: Introductionmentioning

confidence: 99%

Path planning for a UGV using Salp Swarm Algorithm

AlShabi,

Ballous,

Bou Nassif

et al. 2024

Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2024

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The paper researches a utilization of the Salp Swarm Algorithm (SSA), a bio-mimetic optimization technique, to improve path planning in Unmanned Ground Vehicles (UGVs). Because of the crucial role of the efficient and reliable path planning in the implementation of UGVs in such sectors as military, rescue operations, and agriculture, there is a need for algorithms that are capable of navigating complex environments. The concept of SSA, based on the natural swarming behavior of salps, represents a very promising approach that is characterized by the exploration and exploitation properties of the algorithm. This study evaluates the performance of the SSA relative to existing particle swarm optimization (PSO), in terms of path optimality, computational efficiency, and dynamic obstacle adaptability, through a number of simulated environments. Results show that the SSA has the potential to compete with the traditional algorithms in path efficiency and computational load. However, PSO shows slight superiority results compared to SSA. This study highlights the potency of bio-inspired algorithms, specifically the SSA, in enhancing the field of autonomous navigation for UGVs. It introduces new possibilities of practical application of SSA in real-life scenarios, demonstrating its scalability and resilience. The findings of this study make a contribution to the general discussion on the improvement of planning of autonomous routes and provide a possible way for more sustainable and effective UGV activities.

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Employing Robotics and Deep Learning in Underground Leak Detection

Cited by 10 publications

References 30 publications

CCICR-V1.0: An Embeded system for recording Chinese Cuisine Cooker data

CCICR-V1.0: An Embeded system for recording Chinese Cuisine Cooker data

The convergence of control and cognition: a bibliometric overview of UKF in AI-infused robotics

Path planning for a UGV using Salp Swarm Algorithm

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