Autonomous Robotics in Field Operations: A Data-Driven Approach to Optimize Performance and Safety

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@article{1705156,
      author = {Sridevi Kakolu, Muhammad Ashraf Faheem},
      title = {Autonomous Robotics in Field Operations: A Data-Driven Approach to Optimize Performance and Safety},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {4},
      pages = {565-578},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705156.pdf},
      abstract = {Unmanned ground robotics, in particular, has developed a fast and firm foothold in field tasks. It contributes to fieldwork progress, accuracy, and security in vocation types ranging from agricultural to mining and construction bureaus. Using the real-time information gathered, processed, and analyzed through these machines, these robots can do high-end tasks in harsh terrains while requiring limited human input. These focus areas with the specification topic include understanding how data analytics, machine learning, and real-time data processing enhance autonomous robotic performance and operational safety. In the paper, the author also explains how autonomous robotics may be used throughout field operations by conducting a literature review and outlining a precise methodology about how data-driven models are used to forecast, avoid, and accommodate environmental and operational issues. Also, the paper reveals the benefits of applying AI for robotics in the field, such as improving its safety, reducing equipment failures, and maximizing effectiveness. These results advance the field?s prospects for expanding field applications of autonomous robotics and identify improvement areas, including data management, ethical concerns, and fluctuating environmental conditions. This paper also provides recommendations for future work and advancements within autonomous robotics in field operations as the field changes dynamically over time.},
      keywords = {Autonomous Robotics, Field Operations, Data-Driven, Approach, Performance Optimization, Safety Enhancement, Machine Learn},
      month = {October},
  }