Autonomous exploration in large-scale and complex environments is a challenging task. As the size of the environment increases, the significant overhead of exploration algorithms could overwhelm the computational capability of mobile platforms, prohibiting timely response to environmental changes. Meanwhile, the quality of exploration paths becomes increasingly important in larger scenes, as poorly selected paths greatly reduce efficiency. In this letter, a systematic framework is proposed to explore large-scale unknown environments. To enable high-frequency planning, a fast preprocessing of environmental information is presented, providing fundamental information to support high-frequency path planning. An path optimization formulation that comprehensively considers key factors about fast exploration is introduced. Further, an heuristic algorithm is devised to solve the NP-hard optimization problem, which empirically finds optimal solution in real time. Simulation results show the run time of our method is significantly shorter than existing ones. Our method completes exploration with the least time and shortest movement distance compared to current state-of-the-art methods.

The paper presents an air-assisted ground robotic autonomous exploration framework (AAGE), which leverages the high mobility and wide aerial perspective of Unmanned Aerial Vehicles (UAVs) to assist Unmanned Ground Vehicles (UGVs) in detailed exploration, enhancing exploration efficiency and improving the quality of point cloud collection in regions of interest (RoIs) in large-scale, unknown environments. In this framework, the UAV, equipped with an onboard RGB camera, rapidly surveys large unknown areas and generates a Bird's Eye View (BEV) to identify critical zones for UGV exploration. With prior information about the unexplored area's outline from the real-time shared BEV, the UGV can carry out more efficient and informed exploration from a global perspective. To maximize the utility of this prior information and optimize point cloud collection, a hierarchical exploration strategy and an attention mechanism are incorporated to guide the UGV's focus toward areas requiring detailed mapping, rather than broad, featureless regions. Real-world experiments validate the effectiveness of the framework, demonstrating significant improvements in exploration efficiency and point cloud collection compared to state-of-the-art methods. The results further show that even with a coarse BEV, the UGV's exploration efficiency is greatly enhanced.

Autonomous exploration of unknown environments is a challenging problem in robotic applications, especially in large-scale environments. As the size of the environment increases, the limited onboard resources of the robot hardly satisfy the memory overhead and computational requirements. As a result, it is challenging to respond quickly to the received sensor data, resulting in inefficient exploration planning. And it is difficult to comprehensively utilize the gathered environmental information for planning, leading to low-quality exploration paths. In this paper, a systematic framework tailored for unmanned aerial vehicles is proposed to autonomously explore large-scale unknown environments. To reduce memory consumption, a novel low-memory environmental representation is introduced that only maintains the information necessary for exploration. Moreover, a hierarchical exploration approach based on the proposed environmental representation is developed to allow for fast planning and efficient exploration. Extensive simulation tests demonstrate the superiority of the proposed method over current state-of-the-art methods in terms of memory consumption, computation time, and exploration efficiency. Furthermore, two real-world experiments conducted in different large-scale environments also validate the feasibility of our autonomous exploration system.

Autonomous exploration in large-scale, unknown environments remains a significant challenge in mobile robotics. In this letter, we propose a scalable exploration framework that integrates heterogeneous topological representations, lightweight global-local graph reasoning, and reinforcement learning. Our framework is computationally efficient, comprehensively considers global spatial context, and generalizes effectively across diverse environmental scenarios. We introduce a compact topological abstraction to encode crucial spatial and semantic information, substantially reducing map complexity. A novel hybrid inference module, combining linear global attention with local graph convolutions, effectively integrates long-range exploration with comprehensive local coverage. We further design a minimalist reward function paired with a curriculum learning to ensure stable training and enhanced generalization. A viewpoint-based action masking mechanism further refines the action space, accelerating learning convergence. Extensive simulations demonstrate our method consistently surpasses state-of-the-art baselines, achieving up to 14.5% shorter exploration time and 18.8% reduced path length while maintaining low computational overhead. Real-world experiments further validate the practical effectiveness of our approach for robotic exploration tasks.