Autonomous 3D Exploration in Large-Scale Environments with Dynamic Obstacles

Exploration in dynamic and uncertain real-world environments is an open problem in robotics and it constitutes a foundational capability of autonomous systems operating in most of the real-world. While 3D exploration planning has been extensively studied, the environments are assumed static or only reactive collision avoidance is carried out. We propose a novel approach to not only avoid dynamic obstacles but also include them in the plan itself, to deliberately exploit the dynamic environment in the agent's favor. The proposed planner, Dynamic Autonomous Exploration Planner (DAEP), extends AEP to explicitly plan with respect to dynamic obstacles. Furthermore, addressing prior errors within AEP in DAEP has resulted in enhanced exploration within static environments. To thoroughly evaluate exploration planners in such settings we propose a new enhanced benchmark suite with several dynamic environments, including large-scale outdoor environments. DAEP outperforms state-of-the-art planners in dynamic and large-scale environments and is shown to be more effective at both exploration and collision avoidance.