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- W4328011303 abstract "Traditional modular-based path planning methods have certain limitations, they have high latency and computational complexity, and cannot work in unknown environments. Current learning-based path planning methods show significant advantages in solving different planning problems in high-dimensional spaces and complex environments. Learning path planning from demonstrations is a hot research topic. We propose an end-to-end framework based on supervised learning. The end-to-end mapping of unmanned aerial vehicle (UAV) from perception to collision-free trajectories is achieved by learning demonstrations generated based on global path planning methods in a simulated environment. Replacing the traditional modular approach with neural networks can reduce computational latency and prevent error accumulation. Experimental results show that our method has a better performance than traditional path planner." @default.
- W4328011303 created "2023-03-22" @default.
- W4328011303 creator A5039971875 @default.
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- W4328011303 creator A5067808391 @default.
- W4328011303 date "2022-12-09" @default.
- W4328011303 modified "2023-10-16" @default.
- W4328011303 title "Path Planning of Unmanned Aerial Vehicle Based on Supervised Learning" @default.
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- W4328011303 doi "https://doi.org/10.1109/iccc56324.2022.10065740" @default.
- W4328011303 hasPublicationYear "2022" @default.
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