Existing RRT-based exploration methods often suffer from interruptions in the exploration process due to the inability to detect all frontiers of the drivable area in the mapping map. These methods cannot detect all frontiers because the RRT expansion is disturbed by factors such as RRT preset parameters, sliding window constraints, complex external environment, etc, and thus cannot completely cover the drivable area within a limited time. We address this problem by redefining exploration frontiers, designing a novel exploration gain, and constructing minimum RRT search spaces. Our method is evaluated against the existing state-of-the-art RRT-based methods in simulated benchmarks and outdoor environments. The results show that our method is more robust to the above factors while reducing computational cost. Our method is made open source to benefit the community. Index Terms—Autonomous agents, motion and path planning, view planning for SLAM.