Autonomous exploration in complex 3D environments is a key issue in robotics, where current approaches often demonstrate limited efficiency and excessive path backtracking. To mitigate backtracking and repeated exploration in complex multi-channel environments, we propose MCVP (Multi-Channel Viewpoint Planner), an autonomous exploration strategy consisting of three key components: viewpoints generation, viewpoints optimization, and dual-resolution exploration path generation. Firstly, MCVP employs a mixed-cost heuristic function to generate high-quality viewpoints by integrating key factors, such as distance, yaw angle, and positional constraints. Subsequently, a viewpoints optimization process is applied to eliminate redundancies and enhance computational efficiency. To establish an efficient mapping between viewpoints and channels, a bidirectional hash table structure indexed by distance-based criteria is utilized, enabling rapid correspondence retrieval. Finally, the system generates a dual-resolution exploration path, enabling efficient and adaptive navigation for mobile robots in complex environments. We evaluate the proposed method against state-of-the-art approaches in multiple challenging simulation scenarios. The quantitative and qualitative results demonstrate that our method can successfully achieve complete exploration across diverse environments with high efficiency , while exhibiting significant advantages in terms of exploration time and movement distance. To further validate the proposed approach, we conduct real-world experiments in both an underground parking and a complex university campus. The experimental results also further confirm the robustness and practical feasibility of our method in realistic unknown environments.
Zou et al. (2026) studied this question.