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.
This interactive 2D demo captures many of the core ideas behind IA-TIGRIS in a lightweight browser setting: weighted sampling candidate states, estimating path-integrated information gain, tree-recycling, heuristic pruning, etc. It lacks many of the real-world constraints and complexities of the full planner, such as 3D field of view, motion constraints, real-time planning, etc. Fair warning, this was vibe-coded from the source code and paper and is not meant to be a polished demo, but we hope it can be a interactive way to get a better intuition for how the planner works and the impact of different parameters.
@ARTICLE{moon2025iatigris,
title={IA-TIGRIS: An Incremental and Adaptive Sampling-Based Planner for Online Informative Path Planning},
author={Brady Moon and Nayana Suvarna and Andrew Jong and Satrajit Chatterjee and Junbin Yuan and Muqing Cao and Sebastian Scherer},
journal={IEEE Transactions on Robotics},
year={2026},
pages={1-19},
doi={10.1109/TRO.2026.3672542}
}