Year: 2024
Type: article
Abstract: In recent years, the demand for advanced human detection technologies has surged, driven by applications in security, search and rescue operations, and human-robot interaction. This paper presents an innovative approach to human detection using multiple sensor modalities, including infrared (IR), ultrasonic, and visual imaging, integrated into a mobile robotic platform. The proposed system aims to enhance detection accuracy, reduce false positives and improve response times in diverse environments. The robot is equipped with an array of sensors: infrared sensors detect heat signatures, ultrasonic sensors measure distance through sound waves, and visual cameras provide real-time image processing capabilities. The fusion of these data sources is achieved through a multi-layered machine learning algorithm, allowing the robot to make informed decisions based on the contextual information it gathers. This approach not only enhances the robot's ability to detect humans in various conditions such as low light or obstructed views but also enables adaptive learning, allowing the system to improve its accuracy over time through exposure to diverse scenarios. Testing in real-world environments demonstrates the effectiveness of the multi-sensor framework. Results indicate a significant reduction in false detection rates compared to single-sensor systems, highlighting the robustness of the approach. The implementation of this multi-sensor human detection robot has the potential to transform applications in surveillance, safety monitoring, and autonomous assistance, paving the way for more intelligent and responsive robotic systems in the future.
Authors: Akhil Vinod V -, Archa R -, Vinaya V S -, Swetha P R -, Reshmi Krishna Prasad -
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4405979855