Robust Robotic Search and Rescue in Harsh Environments: An Example and Open Challenges

The deployment of autonomous robots in harsh and unpredictable environments has gained significant attention in recent years, driven by the need to improve safety and efficiency in tasks where human involvement poses significant risks. Technological advances in sensors and communication technologies, as well as the proliferation of AI, have catalysed the development of highly capable and robust robotic systems in diverse scenarios, optimising efficiency, particularly in the area of disaster response. However, despite the progress made in the field of autonomous Search and Rescue (SAR) operations in harsh environments, several challenges remain unresolved. This paper presents a comprehensive overview of the key technological advancements in components, algorithms and communication systems and how they facilitate highly efficient and robust autonomous missions in extreme subterranean conditions and SAR operations. Additionally, this paper aims to tackle some of the key challenges encountered during emergency response operations through a novel autonomous robotic design that leverages locomotion, perception, communication, and control capabilities to achieve effective exploration tasks in harsh and hazardous environments. Finally, it sets the scene for future enhancements in the area of autonomous and intelligent robots, suggesting potential avenues for further research and development in enhancing their capabilities and adaptability.

I. Introduction

Advances in robotics and automation can play a crucial role in enhancing and advancing Search and Rescue (SAR), disaster response, and civil protection efforts. Robotic platforms offer the potential to empower first responders, enabling them to accomplish more in less time and with fewer personnel and also with a reduced risk. They can also solve complex autonomous missions in dark or poorly-illuminated, confined, or cluttered environments, therefore providing critical information to first responders and rescue teams during the operations [1]. At the same time, there has been a significant boost in cognitive research fields, including computer vision, machine learning, sensor fusion, obstacle recognition, and planning. These advancements have had a tremendous impact on various real-life applications, including but not limited to autonomous robotics in harsh and GNSS-denied environments.