IA-TIGRIS: An Incremental and Adaptive Sampling-Based Planner for Online Informative Path Planning

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Abstract: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: this https URL

Comments: Published in IEEE Transactions on Robotics, 19 pages, 19 figures

Subjects:

Robotics (cs.RO)

Cite as: arXiv:2502.15961 [cs.RO]

(or arXiv:2502.15961v3 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2502.15961

arXiv-issued DOI via DataCite

Related DOI:

https://doi.org/10.1109/TRO.2026.3672542

DOI(s) linking to related resources

Submission history

From: Brady Moon [view email] [v1] Fri, 21 Feb 2025 21:46:56 UTC (15,378 KB) [v2] Fri, 5 Sep 2025 16:48:02 UTC (15,195 KB) [v3] Wed, 1 Apr 2026 23:28:14 UTC (14,958 KB)