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- W3207399992 abstract "Navigation on challenging terrain topographies requires the understanding of robots’ locomotion capabilities to produce optimal solutions. We present an integrated framework for real-time autonomous navigation of mobile robots based on elevation maps. The framework performs rapid global path planning and optimization that is aware of the locomotion capabilities of the robot. A GPU-aided, sampling-based path planner combined with a gradient-based path optimizer provides optimal paths by using a neural network-based locomotion cost predictor which is trained in simulation. We show that our approach is capable of planning and optimizing paths three orders of magnitude faster than RRT* on GPU-enabled hardware, enabling real-time deployment on mobile platforms. We successfully evaluate the framework on the ANYmal C quadrupedal robot in both simulations and real-world environments for path planning tasks on multiple complex terrains." @default.
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- W3207399992 date "2021-05-30" @default.
- W3207399992 modified "2023-09-25" @default.
- W3207399992 title "Real-time Optimal Navigation Planning Using Learned Motion Costs" @default.
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- W3207399992 doi "https://doi.org/10.1109/icra48506.2021.9561861" @default.
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