Kernel Metadata Declaration — on demand

What the DOI identifies

SURFACE IDENTIFICATION OF ROBOT SENSED DATA AN ARTIFICIAL INTELLIGENCE APPROACH

10.46243/jst.2023.v8.i12.pp120-130 · JournalArticle — an article in a journal · Digital · Visual · Language · en

Published 2023-12-12

Part of Journal of Science & Technology · ISSN 2456-5660 · vol. 8 · no. 12 · pp. 120–130

Principal agents

• Dr. M. Vanitha Dr. M. Vanitha (author → Author)
• Longman Publishers (publisher → Publisher)

Also in the record, outside the Kernel: the abstract, the licence, 4 links, 12 references. Source: Crossref (member 25296), registered 2024-02-16, last deposited 2026-09-17.

⬇ Record (JSON) ⬇ Declaration (XML)

System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1

Each element by the standard's name and the Handbook's (in grey), read off the record.

The record

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        "value": "In recent years, the integration of robotics and artificial intelligence (AI) has gained significant momentum across various industries. Robots equipped with sensors play a crucial role in data acquisition for tasks such as environmental monitoring, industrial automation, and autonomous navigation. Surface identification, specifically the ability to recognize and understand the surfaces in a robot’s environment, is essential for enabling precise and context-aware robotic operations. The history of surface identification in robotics is closely tied to the evolution of computer vision and machine learning. Early robotic systems relied on basic sensor data for navigation, often struggling with accurate perception of the surrounding environment. Over time, advancements in computer vision techniques and AI algorithms have enabled robots to extract meaningful information from sensor data, leading to more sophisticated capabilities, including surface identification. The challenge in surface identification for robot-sensed data lies in developing algorithms that can robustly and accurately differentiate between various surfaces in the environment. This involves recognizing and classifying different types of surfaces such as floors, walls, obstacles, and other objects. Traditional methods often face difficulties in handling complex and dynamic environments, where lighting conditions, object orientations, and material variations can affect the accuracy of surface identification. Traditional systems for surface identification in robot-sensed data often rely on rule-based approaches or simple heuristics. These methods may use thresholding techniques or predefined rules to classify surfaces based on sensor readings. However, these approaches have limitations when faced with the complexity and variability inherent in real-world environments. They may struggle with adaptability to changing conditions and lack the ability to generalize across diverse scenarios. The increasing demand for more sophisticated robotic applications underscores the need for advanced surface identification capabilities. AI approaches, particularly those leveraging deep learning and neural networks, offer the potential to significantly improve the accuracy and robustness of surface identification in robot-sensed data. An artificial intelligence approach to surface identification involves training models, such as convolutional neural networks (CNNs), on labeled datasets containing examples of different surfaces. These models can learn to automatically extract relevant features from sensor data, allowing the robot to discern and classify surfaces with greater accuracy. The use of AI in surface identification enhances adaptability, allowing robots to navigate and interact with their environment more effectively",
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            "doi": "10.1109/mnet.2017.1700133",
            "unstructured": "Wang, J.; Gao, Q.; Pan, M.; Fang, Y. Device-Free Wireless Sensing: Challenges, Opportunities, and Applications. IEEE Netw. 2018, 32, 132–137"
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            "doi": "10.3390/robotics7020017",
            "unstructured": "Zhu, Z.; Hu, H. Robot Learning from Demonstration in Robotic Assembly: A Survey. Robotics 2018, 7, 17"
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        {
            "key": "ref3",
            "doi": "10.1016/j.sna.2011.02.038",
            "unstructured": "Yousef, H.; Boukallel, M.; Althoefer, K. Tactile sensing for dexterous in-hand manipulation in robotics—A review. Sens. Actuators A Phys. 2011, 167, 171–187"
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            "doi": "10.3390/s18082674",
            "unstructured": "Liakos, K.; Busato, P.; Moshou, D.; Pearson, S.; Bochtis, D. Machine Learning in Agriculture: A Review. Sensors 2018, 18, 2674"
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            "key": "ref5",
            "doi": "10.1109/jsen.2012.2208740",
            "unstructured": "Ishida, H.; Wada, Y.; Matsukura, H. Chemical Sensing in Robotic Applications: A Review. IEEE Sens. J. 2012, 12, 3163–3173"
        },
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            "key": "ref6",
            "unstructured": "Andrea, C.; Navarro-Alarcon, D. Sensor-Based Control for Collaborative Robots: Fundamentals, Figure 3: Main GUI application of proposed surface identification of robot sensed data an artificial intelligence approach. Figure 4: Displays the ROC curve graph for the decision tree classifier Figure 5: Displays the comparison of performance metrics of the RFC and Decision Tree models. Figure 6: Displays the prediction of test data in GUI. Dr. M. Vanitha, Ch. Rasmitha, Ch. Sindhu, D. Satvika: SURFACE IDENTIFICATION OF ROBOT SENSED DATA AN ARTIFICIAL INTELLIGENCE APPROACH Challenges, and Opportunities. Front. Neurorobot. 2021, 113, 576846"
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            "key": "ref7",
            "doi": "10.13169/prometheus.38.1.0098",
            "unstructured": "Coggins, T.N. More work for Roomba? Domestic robots, housework and the production of privacy. Prometheus 2022, 38, 98–112"
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            "doi": "10.1016/j.compag.2015.01.013",
            "unstructured": "Blanes, C.; Ortiz, C.; Mellado, M.; Beltrán, P. Assessment of eggplant firmness with accelerometers on a pneumatic robot gripper. Comput. Electron. Agric. 2015, 113, 44–50"
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            "key": "ref9",
            "doi": "10.1177/02783640122067318",
            "unstructured": "Russell, R.A. Survey of Robotic Applications for Odor-Sensing Technology. Int. J. Robot. Res. 2001, 20, 144–162"
        },
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            "key": "ref10",
            "doi": "10.22214/ijraset.2017.10307",
            "unstructured": "Deshmukh, A. Survey Paper on Stereo-Vision Based Object Finding Robot. Int. J. Res. Appl. Sci. Eng. Technol. 2017, 5, 2100–2103"
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            "doi": "10.1016/j.procir.2020.05.214",
            "unstructured": "Deuerlein, C.; Langer, M.; Seßner, J.; Heß, P.; Franke, J. Human-robot-interaction using cloud-based speech recognition systems. Procedia Cirp 2021, 97, 130–135"
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            "doi": "10.1177/0278364914548050",
            "unstructured": "Alameda-Pineda, X.; Horaud, R. Vision-guided robot hearing. Int. J. Robot.Res. 2014, 34, 437–456"
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The Kernel Metadata Declaration

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