In recent years, autonomous robotic exploration has become an increasingly vital capability, particularly in environments where human access is limited, hazardous, or impossible. Examples include post-disaster search-and-rescue missions, subterranean cave and mine inspections, and planetary lava tube explorations. In such scenarios, autonomous systems offer not only enhanced safety but also the ability to rapidly gather critical data that would otherwise be inaccessible, improving both the efficiency and safety of field operations. As robotic systems continue to evolve, their ability to function independently in complex and GPS-denied environments relies on advances in mapping, contextual understanding of the environment and decision making. Meeting these demands requires developing frameworks that are adaptable, reliable, and capable of real-world operation beyond controlled simulation environments.

This thesis presents the development of a series of exploration and navigation frameworks designed to advance autonomous field robotics across diverse platforms, including Unmanned Aerial Vehicles (UAVs), quadrupeds, and ground-based rovers. The first contribution introduces the Rapid Exploration Framework (REF), a frontier-based system that integrates adaptive frontier switching and energy-aware decision-making to ensure safe and efficient exploration in resource-constrained subterranean settings. Building on this foundation, a rolling graph-based exploration strategy is developed, capable of incrementally constructing and updating local subgraphs in real time. This approach enables scalable and memory-efficient global navigation, maintaining continuity and computational efficiency as the environment evolves. The thesis then introduces the Semantics and Traversability-Aware Graph-based Exploration (STAGE) planner, which augments graph-based planning with real-time semantic and traversability assessments. By continuously adapting navigation graphs to reflect environmental changes, STAGE achieves reliable and collision-free motion in dynamic and uncertain terrains. The final stage of this research presents STAGE 2.0, a hierarchical and multimodal exploration system that enables cooperation between aerial and ground robots. By sharing geometric and semantic traversability information through a layered graph representation, ground robots can identify low-traversability or high-risk areas and request aerial support where necessary. This coordination allows aerial robots to extend exploration beyond physical barriers without requiring full map exchanges. The frameworks have been extensively validated through both simulation and real-world field trials in underground mines and natural cave environments, demonstrating their robustness and readiness for practical deployment. The resulting systems contribute directly to real-world applications such as autonomous mine inspection and rapid rockfall assessment, representing a significant step toward deploying intelligent, self-reliant robotic explorers capable of operating in the most demanding and unstructured environments.