SIGNAL PROCESSING IN ROBOT MANIPULATORS: MATHEMATICAL FRAMEWORKS AND APPLICATIONS

Authors

  • Egamberdiev Azimjon Namangan State University of Technology
  • Ismanov Muhammadziyo Namangan State University of Technology

Keywords:

Robot Manipulators, Signal Processing, Sensor Data Acquisition, Kalman Filtering, PID Control, Model Predictive Control (MPC), Noise Reduction, Adaptive Filtering (LMS Algorithm), Wavelet Transform, Euler-Lagrange Dynamics, Jacobian-Based Control, Quantization Error, Nyquist-Shannon Theorem, Real-Time Processing, Sensor Fusion, Latency Mitigation, Industrial Automation, Medical Robotics, AI/ML Integration, Quantization Error.

Abstract

Signal processing is pivotal in enhancing the performance of robot manipulators by ensuring precise sensor data interpretation, noise reduction, and real-time control. This paper explores the role of signal processing in sensor data acquisition, control signal generation, noise mitigation, and real-time implementation. Case studies in industrial and medical applications underscore its impact on accuracy and reliability. Emerging trends, such as AI integration, are discussed to highlight future research directions.

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Published

2025-06-26

Issue

Vol. 40 (2025)

Section

Articles

How to Cite

SIGNAL PROCESSING IN ROBOT MANIPULATORS: MATHEMATICAL FRAMEWORKS AND APPLICATIONS. (2025). Spectrum Journal of Innovation, Reforms and Development, 40, 99-109. https://sjird.journalspark.org/index.php/sjird/article/view/1253