Efficient Frontier-Sampling-Mixed Autonomous Exploration Using Environmental Complexity

Abstract

When exploring complex unknown environments, unmanned aerial vehicles (UAVs) often experience reduced efficiency and robustness due to unevenly distributed occlusions. This paper proposes an efficient hybrid autonomous exploration algorithm that adapts to environmental complexity, enabling effective frontier detection and viewpoint sampling to minimize overall exploration time. We introduce a frontier detection method based on a limited field of view (FOV), along with an unique ID-based frontier management mechanism, which ensures detection completeness while significantly reducing computational and memory overhead. Furthermore, an adaptive sampling strategy incorporating environmental complexity is introduced. By adaptively switching sampling modes and relaxing obstacle-free sphere generation constraints, the method improves both sampling efficiency and visibility evaluation performance. For path planning, a hierarchical planner based on a topological graph is constructed. It jointly optimizes global coverage paths and local frontier information to generate smooth and time-optimal trajectories. Both simulation and real-world experiments validate the advantages of the proposed approach in terms of exploration efficiency, computational overhead, and coverage rate.