Off-road autonomous navigation faces a significant challenge due to the lack of maps or road markings for planning paths. Classical path planning methods assume a perfectly known envi- ronment, neglecting the inherent perception and sensing uncertainty from detecting terrain and obstacles in off-road environments. This research proposes an uncertainty-aware path planning method, URA*, using aerial images for autonomous navigation in off-road environments. An ensemble convolutional neural network model is used to perform pixel-level traversability estima- tion from aerial images of the region of interest. Traversability predictions are represented as a grid of traversal probability values. An uncertainty-aware planner is applied to compute the best path from a start point to a goal point, considering these noisy traversal probability estimates. The proposed planner also incorporates replanning techniques for rapid replacement during online robot operation. The method is evaluated on the Massachusetts Road Dataset, DeepGlobe dataset, and aerial images from CAVS proving grounds at MSU.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-7154 |
| Date | 10 May 2024 |
| Creators | Moore, Charles Alan |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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