Smart Robotic Arm-Based Broom with Dust Detection Using IoT and Computer Vision

The demand for cleaner, more efficient, and self-sufficient indoor spaces is rapidly increasing. Traditional manual cleaning methods are inconsistent and time-consuming, while most commercial robotic vacuum cleaners follow rigid, pre-set routes and lack intelligent dirt detection. However, advancements at the intersection of robotics, IoT, and computer vision are paving the way for unsupervised cleaning systems capable of sensing and responding to dirt in real time. This survey focuses on the development of a smart robotic arm equipped with a broom and integrated IoT-based computer vision for dust detection. We comprehensively review dust-cleaning robotic arms, computer vision techniques for dirt detection, remote monitoring via IoT, and autonomous indoor navigation. The evolution of cleaning robotic arms is traced from basic mechanical designs to sophisticated, vision-based systems that can identify dirt, utilize advanced algorithms for path optimization, and target cleaning with intelligent sweeping mechanisms. Research highlights the effectiveness of computer vision in recognizing dirt patterns, while IoT technologies enable real-time remote control and monitoring. Path-planning algorithms like A* further enhance navigation in complex environments. Although challenges such as lighting conditions, reflective surfaces, and mechanical constraints persist, the integration of these technologies holds significant promise for achieving truly autonomous and efficient indoor cleaning. This survey synthesizes key research trends and technological innovations that directly inform the design and implementation of the advanced automated floor-cleaning system developed for the capstone project.