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.

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In Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Stockholm, Sweden, July 2018.

@inproceedings{AAMAS18-yunl, title={PETLON - Planning Efficiently for Task-Level Optimal Navigation}, author={Shih-Yun Lo and Shiqi Zhang and Peter Stone}, booktitle={Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)}, month={July}, address={Stockholm, Sweden}, url="http://www.cs.utexas.edu/users/ai-lab?AAMAS18-yunl", year={2018} }