IA-TIGRIS: An Incremental and Adaptive Sampling-Based Planner for Online Informative Path Planning
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arXiv
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| Format: | Preprint |
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2025
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| author | Moon, Brady Suvarna, Nayana Jong, Andrew Chatterjee, Satrajit Yuan, Junbin Cao, Muqing Scherer, Sebastian |
| author_facet | Moon, Brady Suvarna, Nayana Jong, Andrew Chatterjee, Satrajit Yuan, Junbin Cao, Muqing Scherer, Sebastian |
| contents | Planning paths that maximize information gain for robotic platforms has wide-ranging applications and significant potential impact. To effectively adapt to real-time data collection, informative path planning must be computed online and be responsive to new observations. In this work, we present IA-TIGRIS (Incremental and Adaptive Tree-based Information Gathering Using Informed Sampling), which is an incremental and adaptive sampling-based informative path planner designed for real-time onboard execution. Our approach leverages past planning efforts through incremental refinement while continuously adapting to updated belief maps. We additionally present detailed implementation and optimization insights to facilitate real-world deployment, along with an array of reward functions tailored to specific missions and behaviors. Extensive simulation results demonstrate IA-TIGRIS generates higher-quality paths compared to baseline methods. We validate our planner on two distinct hardware platforms: a hexarotor unmanned aerial vehicle (UAV) and a fixed-wing UAV, each having different motion models and configuration spaces. Our results show up to a 38% improvement in information gain compared to baseline methods, highlighting the planner's potential for deployment in real-world applications. Project website: https://ia-tigris.github.io |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_15961 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | IA-TIGRIS: An Incremental and Adaptive Sampling-Based Planner for Online Informative Path Planning Moon, Brady Suvarna, Nayana Jong, Andrew Chatterjee, Satrajit Yuan, Junbin Cao, Muqing Scherer, Sebastian Robotics Planning paths that maximize information gain for robotic platforms has wide-ranging applications and significant potential impact. To effectively adapt to real-time data collection, informative path planning must be computed online and be responsive to new observations. In this work, we present IA-TIGRIS (Incremental and Adaptive Tree-based Information Gathering Using Informed Sampling), which is an incremental and adaptive sampling-based informative path planner designed for real-time onboard execution. Our approach leverages past planning efforts through incremental refinement while continuously adapting to updated belief maps. We additionally present detailed implementation and optimization insights to facilitate real-world deployment, along with an array of reward functions tailored to specific missions and behaviors. Extensive simulation results demonstrate IA-TIGRIS generates higher-quality paths compared to baseline methods. We validate our planner on two distinct hardware platforms: a hexarotor unmanned aerial vehicle (UAV) and a fixed-wing UAV, each having different motion models and configuration spaces. Our results show up to a 38% improvement in information gain compared to baseline methods, highlighting the planner's potential for deployment in real-world applications. Project website: https://ia-tigris.github.io |
| title | IA-TIGRIS: An Incremental and Adaptive Sampling-Based Planner for Online Informative Path Planning |
| topic | Robotics |
| url | https://arxiv.org/abs/2502.15961 |