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- W3129762237 abstract "Reliable real-time planning for robots is essential in today's rapidly expanding automated ecosystem. In such environments, traditional methods that plan by relaxing constraints become unreliable or slow-down for kinematically constrained robots. This paper describes the algorithm Dynamic Motion Planning Networks (Dynamic MPNet), an extension to Motion Planning Networks, for non-holonomic robots that address the challenge of real-time motion planning using a neural planning approach. We propose modifications to the training and planning networks that make it possible for real-time planning while improving the data efficiency of training and trained models' generalizability. We evaluate our model in simulation for planning tasks for a non-holonomic robot. We also demonstrate experimental results for an indoor navigation task using a Dubins car." @default.
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- W3129762237 date "2020-10-24" @default.
- W3129762237 modified "2023-10-06" @default.
- W3129762237 title "Dynamically Constrained Motion Planning Networks for Non-Holonomic Robots" @default.
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- W3129762237 doi "https://doi.org/10.1109/iros45743.2020.9341283" @default.
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