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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.10475 |
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| _version_ | 1866917430636314624 |
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| author | Duggan, Cora A. Goertz, Adam Polevoy, Adam Gonzales, Mark Wolfe, Kevin C. Woosley, Bradley Rogers III, John G. Moore, Joseph |
| author_facet | Duggan, Cora A. Goertz, Adam Polevoy, Adam Gonzales, Mark Wolfe, Kevin C. Woosley, Bradley Rogers III, John G. Moore, Joseph |
| contents | In this paper, we present Stratified Topological Autonomy for Long-Range Coordination (STALC), a hierarchical planning approach for multi-robot coordination in real-world environments with significant inter-robot spatial and temporal dependencies. At its core, STALC consists of a multi-robot graph-based planner which combines a topological graph with a novel, computationally efficient mixed-integer programming formulation to generate highly-coupled multi-robot plans in seconds. To enable autonomous planning across different spatial and temporal scales, we construct our graphs so that they capture connectivity between free-space regions and other problem-specific features, such as traversability or risk. We then use receding-horizon planners to achieve local collision avoidance and formation control. To evaluate our approach, we consider a multi-robot reconnaissance scenario where robots must autonomously coordinate to navigate through an environment while minimizing the risk of detection by observers. Through simulation-based experiments, we show that our approach is able to scale to address complex multi-robot planning scenarios. Through hardware experiments, we demonstrate our ability to generate graphs from real-world data and successfully plan across the entire hierarchy to achieve shared objectives. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_10475 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stratified Topological Autonomy for Long-Range Coordination (STALC) Duggan, Cora A. Goertz, Adam Polevoy, Adam Gonzales, Mark Wolfe, Kevin C. Woosley, Bradley Rogers III, John G. Moore, Joseph Robotics Systems and Control In this paper, we present Stratified Topological Autonomy for Long-Range Coordination (STALC), a hierarchical planning approach for multi-robot coordination in real-world environments with significant inter-robot spatial and temporal dependencies. At its core, STALC consists of a multi-robot graph-based planner which combines a topological graph with a novel, computationally efficient mixed-integer programming formulation to generate highly-coupled multi-robot plans in seconds. To enable autonomous planning across different spatial and temporal scales, we construct our graphs so that they capture connectivity between free-space regions and other problem-specific features, such as traversability or risk. We then use receding-horizon planners to achieve local collision avoidance and formation control. To evaluate our approach, we consider a multi-robot reconnaissance scenario where robots must autonomously coordinate to navigate through an environment while minimizing the risk of detection by observers. Through simulation-based experiments, we show that our approach is able to scale to address complex multi-robot planning scenarios. Through hardware experiments, we demonstrate our ability to generate graphs from real-world data and successfully plan across the entire hierarchy to achieve shared objectives. |
| title | Stratified Topological Autonomy for Long-Range Coordination (STALC) |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2503.10475 |