A Comprehensive Review on Scalable Arduino Radar Platform for Real-time Object Detection and Mapping
Keywords:
Arduino, Environmental adaptability, Integration challenges, Machine learning, Mapping algorithms, Radar technology, Real-time object detection, Scalable systems, Sensor fusionAbstract
This study provides a comprehensive review of the scalable Arduino radar platform, emphasizing its potential for real-time object detection and environmental mapping in resource-constrained environments. The study highlights the integration of machine learning algorithms, sensor fusion techniques, and signal processing methods to enhance object detection accuracy and mapping precision. A comparative analysis of existing radar platforms is conducted, focusing on factors such as detection range, processing speed, cost, and power efficiency. For example, Arduino-based systems are benchmarked against Raspberry Pi and commercial radar units, revealing that Arduino achieves over 90% detection accuracy with lower power consumption and reduced cost, though with limited processing capacity. Mapping algorithms like occupancy grid and SLAM are evaluated based on performance in cluttered and dynamic environments, with Arduino systems showing acceptable resolution but constrained by memory limits. The paper also reviews prototype implementations and simulation results to assess real-time tracking efficiency and scalability. Challenges such as integration complexity and hardware limitations are discussed alongside potential improvements. This analysis establishes Arduino radar as a viable, low-cost solution for applications in robotics, automation, and environmental monitoring while offering a roadmap for future enhancements in algorithmic optimization and hardware interfacing.
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