Survey on Reconnaissance Autonomous Robotic Systems for Disaster Management

TL;DR

This survey reviews recent advancements in autonomous ground robots for disaster management, focusing on hardware, software, and challenges like energy use and benchmarking.

Contribution

The paper provides a comprehensive analysis of 190 recent studies on disaster reconnaissance robots, highlighting progress and remaining challenges in the field.

Findings

There has been significant progress in navigation, sensor fusion, and situational awareness in disaster reconnaissance robots.

Energy limitations and lack of standardized benchmarks remain major challenges for autonomous robotic systems in disaster scenarios.

Abstract

Systems that operate in dangerous environments are becoming essential in case of emergencies. This survey reviews the latest ground reconnaissance robots using computer vision (CV), machine learning (ML), MCU-based control, LoRa communication, DC motors, and dual-power systems. The analysis includes hardware and algorithms, and their performance in the field and lab. There has been clear progress in navigation, sensor fusion, and situational awareness. The main challenges which remain include the use of energy and standardization of benchmarks. This survey focuses exclusively on Unmanned Ground Vehicles (UGVs) for disaster reconnaissance, examining recent advances in hardware, software, and autonomy. The survey highlights the improvements in navigation, sensor fusion, and intelligence, and identifies remaining challenges such as energy limitations, robustness in harsh conditions, and the lack of standardized benchmarks. The analysis synthesizes findings from over 190 recent studies (2020–2025) in ground-based disaster robotics, providing a comprehensive overview of current capabilities and research gaps. It encapsulates all issues with their remedy for future disaster-response systems.

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