PETLON: Planning Efficiently for Task-Level-Optimal Navigation
PETLON: Planning Efficiently for Task-Level-Optimal Navigation
复制标题
PETLON：有效规划任务级最优导航
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
P. Stone
中科院分区：
文献类型：
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作者：
Shih;Shiqi Zhang;P. Stone
智能移动的机器人最近已经能够在大规模室内环境中长时间自主操作。在这样的环境中的任务规划涉及排序机器人的高级目标和子目标，并且通常需要对环境中的人、房间和物体的位置以及它们的交互进行推理以实现目标。最佳任务规划的先决条件之一是准确估计机器人从一个位置导航到另一个位置所需的实际距离（或时间）。国家的最先进的运动规划，虽然往往计算复杂，正是为了这个目的，通过有限的空间找到路线。在这项工作中，我们专注于集成任务和运动规划（TMP），以实现任务级的机器人导航的最佳规划，同时保持可管理的计算效率。为此，我们引入TMP算法PETLON（规划有效的任务级最佳导航）的日常服务任务，使用移动的机器人。PETLON比预先计算所有可能的导航动作的运动成本的规划方法更有效，同时仍然生成在任务级别上最优的计划。
Intelligent mobile robots have recently become able to operate autonomously in large-scale indoor environments for extended periods of time. Task planning in such environments involves sequencing the robot's high-level goals and subgoals, and typically requires reasoning about the locations of people, rooms, and objects in the environment, and their interactions to achieve a goal. One of the prerequisites for optimal task planning that is often overlooked is having an accurate estimate of the actual distance (or time) a robot needs to navigate from one location to another. State-of-the-art motion planners, though often computationally complex, are designed exactly for this purpose of finding routes through constrained spaces. In this work, we focus on integrating task and motion planning (TMP) to achieve task-level optimal planning for robot navigation while maintaining manageable computational efficiency. To this end, we introduce TMP algorithm PETLON (Planning Efficiently for Task-Level-Optimal Navigation) for everyday service tasks using a mobile robot. PETLON is more efficient than planning approaches that pre-compute motion costs of all possible navigation actions, while still producing plans that are optimal at the task level.