SIGNAL PROCESSING IN ROBOT MANIPULATORS: MATHEMATICAL FRAMEWORKS AND APPLICATIONS - Egamberdiev Azimjon; Ismanov Muhammadziyo (Journals Park Publishing, 2025)
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.
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.