Exploration in dynamic and uncertain real-world environments is an open problem in robotics and 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 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. 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 outperform state-of-the-art planners in dynamic and large-scale environments. DAEP is shown to be more effective at both exploration and collision avoidance.

翻译：在动态且不确定的真实世界环境中进行探索是机器人领域的一个开放性难题，也是大多数真实世界中自主系统运行的基础能力。尽管三维探索规划已得到广泛研究，但现有方法均假设环境为静态，或仅进行反应式避障。我们提出了一种新颖方法，不仅能够规避动态障碍物，还能将其纳入规划本身，从而利用动态环境为智能体创造优势。所提出的规划器——动态自主探索规划器DAEP——通过扩展AEP，明确实现了针对动态障碍物的规划。为在此类场景中全面评估探索规划器，我们提出了一套新的增强型基准测试套件，包含多种动态环境（包括大规模室外环境）。实验表明，DAEP在动态及大规模环境下均优于当前最先进的规划器，在探索效率和避障能力上表现更为出色。