MTG: Mapless Trajectory Generator with Traversability Coverage for Outdoor Navigation
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866910351894773760 |
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| author | Liang, Jing Gao, Peng Xiao, Xuesu Sathyamoorthy, Adarsh Jagan Elnoor, Mohamed Lin, Ming C. Manocha, Dinesh |
| author_facet | Liang, Jing Gao, Peng Xiao, Xuesu Sathyamoorthy, Adarsh Jagan Elnoor, Mohamed Lin, Ming C. Manocha, Dinesh |
| contents | We present a novel learning-based trajectory generation algorithm for outdoor robot navigation. Our goal is to compute collision-free paths that also satisfy the environment-specific traversability constraints. Our approach is designed for global planning using limited onboard robot perception in mapless environments while ensuring comprehensive coverage of all traversable directions. Our formulation uses a Conditional Variational Autoencoder (CVAE) generative model that is enhanced with traversability constraints and an optimization formulation used for the coverage. We highlight the benefits of our approach over state-of-the-art trajectory generation approaches and demonstrate its performance in challenging and large outdoor environments, including around buildings, across intersections, along trails, and off-road terrain, using a Clearpath Husky and a Boston Dynamics Spot robot. In practice, our approach results in a 6% improvement in coverage of traversable areas and an 89% reduction in trajectory portions residing in non-traversable regions. Our video is here: https://youtu.be/3eJ2soAzXnU |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_08214 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | MTG: Mapless Trajectory Generator with Traversability Coverage for Outdoor Navigation Liang, Jing Gao, Peng Xiao, Xuesu Sathyamoorthy, Adarsh Jagan Elnoor, Mohamed Lin, Ming C. Manocha, Dinesh Robotics We present a novel learning-based trajectory generation algorithm for outdoor robot navigation. Our goal is to compute collision-free paths that also satisfy the environment-specific traversability constraints. Our approach is designed for global planning using limited onboard robot perception in mapless environments while ensuring comprehensive coverage of all traversable directions. Our formulation uses a Conditional Variational Autoencoder (CVAE) generative model that is enhanced with traversability constraints and an optimization formulation used for the coverage. We highlight the benefits of our approach over state-of-the-art trajectory generation approaches and demonstrate its performance in challenging and large outdoor environments, including around buildings, across intersections, along trails, and off-road terrain, using a Clearpath Husky and a Boston Dynamics Spot robot. In practice, our approach results in a 6% improvement in coverage of traversable areas and an 89% reduction in trajectory portions residing in non-traversable regions. Our video is here: https://youtu.be/3eJ2soAzXnU |
| title | MTG: Mapless Trajectory Generator with Traversability Coverage for Outdoor Navigation |
| topic | Robotics |
| url | https://arxiv.org/abs/2309.08214 |