Abstract
Autonomous navigation across heterogeneous environments remains challenging because different environments may require fundamentally different planning representations and action models. Structured roads are naturally represented as graphs with constrained connectivity, unstructured terrain requires continuous geometric reasoning, and service-specific areas such as charging stations can be described by discrete task states and symbolic actions. We propose a modular, context-aware planning framework that represents the environment as a set of navigation contexts, each with its own environment representation, planning state space, applicable actions, and dynamics. Contexts are connected through explicitly defined interfaces that determine where inter-context transitions can occur and how states are mapped between the corresponding representations. Planning is formulated as an incremental A*-style search over a joint state space comprising the current context, context-specific state, and internal resource variables such as battery level. The planner jointly considers intra-context actions and inter-context transitions while minimizing accumulated action and context-transition costs subject to modeled resource constraints. We evaluate the framework in simulated environments combining continuous geometric, graph-based, and discrete task-level representations. Compared with a hierarchical multi-context planning baseline using fixed representative interface states, the proposed method produced shorter routes, retained more battery at the goal, and required less planning time in both evaluated resource scenarios. In a restricted grid–topological evaluation, both the proposed method and a strengthened topological baseline found the shortest path in all 50 test cases, while the proposed method achieved lower average planning time. A nearest-node topological baseline frequently failed or produced suboptimal paths.