4 accepted papers
Autonomous exploration is essential for the effective deployment of quadrotors in various applications. However, existing approaches face significant challenges in large-scale environments, particularly in balancing global coverage efficiency and computational overhead. These limitations often resul
Autonomous exploration in large-scale, unknown environments remains a significant challenge in mobile robotics. In this paper, we propose a scalable exploration framework that integrates heterogeneous topological representations, lightweight global-local graph reasoning, and reinforcement learning.
Autonomous exploration in unknown environments is a crucial challenge for various applications of unmanned aerial vehicles (UAVs). However, in large-scale scenarios, existing methods suffer from inefficient environmental information acquisition, computationally expensive exploration planning, and in
In this letter, we study the problem of using mobile robots as relay nodes to keep moving clients and a fixed base station connected. Some applications, such as exploration and rescue, require this problem to be solved. However, existing methods are computationally time-consuming and insufficient to