CONSTRUCTING SENSOR SIGNAL PROCESSING CHANNEL FOR AUTONOMOUS ROBOTIC PLATFORMS
DOI:
https://doi.org/10.15588/1607-3274-2024-4-17Keywords:
autonomous mobile robotic platform, frequency-dependent components, filters, frequency characteristics, cyberphysical systemAbstract
Context. The development of autonomous mobile robotic platforms has advanced rapidly, especially in cyber-physical systems where integrating physical components and computational resources is vital. A key challenge in such platforms is the efficient realtime processing of sensor signals under limited computational resources, enabling robots to operate independently of human intervention. Traditional signal processing methods demand significant power, which may limit mobile platforms constrained by energy and resources. This research focuses on restructuring sensor signal processing channels using digital bandpass filters while overcoming technical challenges posed by limited resources.
Objective. The goal is to create an efficient method for processing sensor signals in autonomous mobile platforms with constrained resources. This involves using low-order bandpass filters, capable of adjusting their characteristics and improving quality through sequential connection of identical filters. Reducing the computational load allows for enhanced overall performance of cyber-physical systems, improving efficiency under changing conditions and enabling autonomous task completion. New computational formulas are also proposed to simplify the design and better utilize onboard resources.
Method. The improved method for constructing sensor signal processing channels uses identical low-order frequency-dependent components, sequentially connected to solve challenges faced by higher-order components. This approach simplifies coefficient calculations for cutoff frequencies and improves filter performance by increasing the order and quality. The method achieves a quasilinear phase-frequency characteristic, ensuring minimal distortion in the processed signals, while significantly reducing computational requirements.
Results. The proposed method effectively reduces computational costs while maintaining high performance in sensor signal processing. The new formulas allow for calculating filter coefficients with fewer resources, suitable for autonomous systems. Modelling and experimental verification confirm that this method lowers the computational load and enhances filter performance, enabling more efficient sensor data processing, extended battery life, and improved system reliability.
Conclusions. This research presents an efficient approach to sensor signal processing for resource-constrained autonomous robotic platforms. Sequentially connecting identical frequency-dependent components reduces computational costs while maintaining high signal processing quality. These findings are recommended for real-time applications requiring efficient resource utilization, contributing to improved autonomy and adaptability in mobile robotic systems.
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