We present a method for solving the coverage problem with the objective of autonomously exploring an unknown environment under mission time constraints. Here, the robot is tasked with planning a path over a horizon such that the accumulated area swept out by its sensor footprint is maximized. Because this problem exhibits a diminishing returns property known as submodularity, we choose to formulate it as a tree-based sequential decision making process. This formulation allows us to evaluate the effects of the robot's actions on future world coverage states, while simultaneously accounting for traversability risk and the dynamic constraints of the robot. To quickly find near-optimal solutions, we propose an effective approximation to the coverage sensor model which adapts to the local environment. Our method was extensively tested across various complex environments and served as the local exploration algorithm for a competing entry in the DARPA Subterranean Challenge.

翻译：我们提出一种解决覆盖问题的方案，目标是在任务时间约束下自主探索未知环境。机器人需要规划一段时域路径，使其传感器覆盖区域累计面积最大化。由于该问题具有称为子模性的收益递减特性，我们选择将其建模为基于树的序列决策过程。该框架允许我们评估机器人动作对未来环境覆盖状态的影响，同时兼顾可通行风险与机器人动力学约束。为了快速获取近似最优解，我们提出一种自适应局部环境的覆盖传感器模型有效近似方法。该方法在多种复杂环境中进行了广泛验证，并作为参赛算法应用于DARPA地下挑战赛的竞争性方案。