Autonomous platforms are critical for accelerating disaster response by delivering situational awareness and search-and-rescue support without exposing human operators to risk. However, practitioners face significant challenges in selecting and implementing robust software on vendor-constrained, immutable hardware. This paper provides a comprehensive survey contrasting the capabilities of two complementary unmanned platforms: Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs). We analyze state-of-the-art software blueprints for perception, navigation, and coordination under the constraints of fixed hardware. Key contributions include a comparative analysis of mission suitability, a synthesis of emerging machine learning algorithms for robust navigation, and an identification of critical research gaps. While recent works have advanced specific algorithms, a comprehensive survey comparing software-driven approaches on fixed-hardware UAVs and UGVs is lacking, a gap this paper aims to fill. Our analysis reveals that the sim-to-real transfer gap, the absence of standardised disaster benchmarks, and limited explainability of deep-reinforcement-learning policies remain the most critical barriers to field deployment. We conclude with a prioritised research roadmap that groups open challenges into short-term (1–2 year) and long-term (3–5+ year) directions.
Jamwal et al. (Mon,) studied this question.