We present an integrated Task-Motion Planning (TMP) framework for navigation in large-scale environments. Of late, TMP for manipulation has attracted significant interest resulting in a proliferation of different approaches. In contrast, TMP for navigation has received considerably less attention. Autonomous robots operating in real-world complex scenarios require planning in the discrete (task) space and the continuous (motion) space. In knowledge-intensive domains, on the one hand, a robot has to reason at the highest-level, for example, the objects to procure, the regions to navigate to in order to acquire them; on the other hand, the feasibility of the respective navigation tasks have to be checked at the execution level. This presents a need for motion-planning-aware task planners. In this paper, we discuss a probabilistically complete approach that leverages this task-motion interaction for navigating in large knowledge-intensive domains, returning a plan that is optimal at the task-level. The framework is intended for motion planning under motion and sensing uncertainty, which is formally known as belief space planning. The underlying methodology is validated in simulation, in an office environment and its scalability is tested in the larger Willow Garage world. A reasonable comparison with a work that is closest to our approach is also provided. We also demonstrate the adaptability of our approach by considering a building floor navigation domain. Finally, we also discuss the limitations of our approach and put forward suggestions for improvements and future work.

我们提出了用于大型环境中导航的集成任务动作计划（TMP）框架。最近，用于操纵的TMP引起了人们的极大兴趣，导致了各种方法的泛滥。相比之下，用于导航的TMP受到的关注却很少。在现实世界中复杂场景中运行的自主机器人需要在离散（任务）空间和连续（运动）空间中进行规划。一方面，在知识密集型领域中，机器人必须在最高层次上进行推理，例如，要采购的对象，要导航的区域才能获取它们。另一方面，必须在执行级别上检查各个导航任务的可行性。这提出了对运动计划感知任务计划器的需求。在本文中，我们讨论了一种概率完整的方法，该方法利用此任务-动作交互在大型知识密集型领域中进行导航，并返回在任务级别上最佳的计划。该框架旨在用于运动和感知不确定性下的运动计划，这正式称为信念空间计划。在办公环境中，基本方法论已在仿真中得到验证，其可扩展性已在更大的Willow Garage世界中进行了测试。还提供了与最接近我们方法的作品的合理比较。我们还通过考虑建筑物楼层导航域来证明我们方法的适应性。最后，我们还讨论了我们方法的局限性，并提出了改进和未来工作的建议。