Citation

Zafar MH, Moosavi SKR, Sanfilippo F. Front. Robot. AI 2024; 11: e1356345.

Copyright

(Copyright © 2024, Frontiers Media)

DOI

10.3389/frobt.2024.1356345

PMID

38957217

PMCID

PMC11217714

Abstract

In this study, we address the critical need for enhanced situational awareness and victim detection capabilities in Search and Rescue (SAR) operations amidst disasters. Traditional unmanned ground vehicles (UGVs) often struggle in such chaotic environments due to their limited manoeuvrability and the challenge of distinguishing victims from debris. Recognising these gaps, our research introduces a novel technological framework that integrates advanced gesture-recognition with cutting-edge deep learning for camera-based victim identification, specifically designed to empower UGVs in disaster scenarios. At the core of our methodology is the development and implementation of the Meerkat Optimization Algorithm-Stacked Convolutional Neural Network-Bi-Long Short Term Memory-Gated Recurrent Unit (MOA-SConv-Bi-LSTM-GRU) model, which sets a new benchmark for hand gesture detection with its remarkable performance metrics: accuracy, precision, recall, and F1-score all approximately 0.9866. This model enables intuitive, real-time control of UGVs through hand gestures, allowing for precise navigation in confined and obstacle-ridden spaces, which is vital for effective SAR operations. Furthermore, we leverage the capabilities of the latest YOLOv8 deep learning model, trained on specialised datasets to accurately detect human victims under a wide range of challenging conditions, such as varying occlusions, lighting, and perspectives. Our comprehensive testing in simulated emergency scenarios validates the effectiveness of our integrated approach. The system demonstrated exceptional proficiency in navigating through obstructions and rapidly locating victims, even in environments with visual impairments like smoke, clutter, and poor lighting. Our study not only highlights the critical gaps in current SAR response capabilities but also offers a pioneering solution through a synergistic blend of gesture-based control, deep learning, and purpose-built robotics. The key findings underscore the potential of our integrated technological framework to significantly enhance UGV performance in disaster scenarios, thereby optimising life-saving outcomes when time is of the essence. This research paves the way for future advancements in SAR technology, with the promise of more efficient and reliable rescue operations in the face of disaster.

Language: en

Keywords

deep learning; disaster response; human detection; multi-modal fusion; unmanned ground vehicles (UGVs)