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Survey on Reconnaissance Autonomous Robotic Systems for Disaster Management
Sahaj Sinha1, Sinjae Lee2, Saurabh Singh1
1AI and Big Data, Endicott College, Woosong University, Daejeon 34606, Republic of Korea.
Sensors (Basel, Switzerland)
|March 14, 2026
Summary
This survey reviews the latest disaster reconnaissance robots, highlighting progress in navigation and sensor fusion. Key challenges remain in energy efficiency and standardized benchmarks for these unmanned ground vehicles (UGVs).
Area of Science:
- Robotics
- Artificial Intelligence
- Computer Vision
Background:
- Emergencies necessitate systems operating in dangerous environments.
- Unmanned Ground Vehicles (UGVs) are crucial for disaster reconnaissance.
- Recent advancements focus on enhancing UGVs' capabilities for response.
Purpose of the Study:
- To survey the latest advancements in ground-based disaster robotics.
- To analyze hardware, software, and autonomy in recent UGVs.
- To identify progress and remaining challenges in the field.
Main Methods:
- Review of over 190 studies from 2020-2025.
- Analysis of computer vision (CV), machine learning (ML), and control systems.
- Evaluation of LoRa communication, DC motors, and dual-power systems.
Main Results:
- Significant progress observed in UGV navigation, sensor fusion, and situational awareness.
- Improvements noted in hardware, software, and autonomy for disaster response.
- Recent studies demonstrate enhanced intelligence in robotic systems.
Conclusions:
- UGVs show clear advancements for disaster reconnaissance.
- Remaining challenges include energy usage and benchmark standardization.
- Future disaster-response systems require solutions for robustness and efficiency.
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