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An Extensive Study of Convolutional Neural Networks: Applications in Computer Vision for Improved Robotics

Ravi Raj1, Andrzej Kos2

  • 1Institute of Robotics and Machine Intelligence, Poznań University of Technology, ul. Piotrowo 3A, 60-965 Poznań, Poland.

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|February 26, 2025
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Summary
This summary is machine-generated.

Convolutional neural networks (CNNs) are powerful deep learning tools for computer vision in robotics. This review explores CNN fundamentals, applications in robotic perception, and future directions for enhanced robot intelligence.

Keywords:
artificial intelligence (AI)computer visionconvolutional neural network (CNN)deep learning (DL)machine learning (ML)mobile robot (MR)perception

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Robotics

Background:

  • Convolutional Neural Networks (CNNs), a type of deep learning artificial neural network (ANN), excel in computer vision tasks.
  • Existing reviews often overlook the specific applications and challenges of CNNs in robotic perception.
  • Robotic perception is a growing field benefiting from advanced AI techniques.

Purpose of the Study:

  • To provide a comprehensive overview of CNNs, focusing on their principles and applications in robotic perception.
  • To address the current research gaps by including recent advancements, particularly in robotic perception.
  • To discuss the challenges and future prospects of using CNNs for improved robotic intelligence.

Main Methods:

  • Review of fundamental principles of CNNs, including convolutional, pooling, and fully connected layers.
  • Analysis of CNNs' application in diverse computer vision tasks relevant to robotics.
  • Exploration of backpropagation as a key mechanism for feature acquisition in CNNs.

Main Results:

  • CNNs offer effective spatial pattern acquisition for autonomous systems.
  • Applications span various computer vision tasks crucial for robotic perception.
  • Understanding CNNs is key to unlocking their potential in enhancing robotic performance.

Conclusions:

  • CNNs are pivotal for advancing robotic perception and intelligence.
  • Further research is needed to address challenges and exploit future prospects in this domain.
  • This review provides a foundational understanding for leveraging CNNs in robotics.