Temporally static environment coverage with offline planning techniques

Complete coverage path planning remains a challenge for environments that change over time. Currently, computationally expensive online, sensor-based planning algorithms are used to address the uncertainty caused by any changes to an environment. Temporally static environments, common in many robot coverage tasks such as cleaning or maintenance surveying, see changes only over extended periods of time. Enhancing offline methods to handle such variations is ideal in order to maintain guaranteed optimality and completeness. This work presents a method of enhancing offline coverage path planning algorithms for temporally static maps through the introduction of adaptive permanence for offline planning decisions. The presentation will include a review of offline and online methods as well as a discussion of how to combine their strengths to address the described situation. It will include any developed algorithms as well as experimental results or demonstrations of the proposed coverage algorithm.