IA-TIGRIS: An Incremental and Adaptive Sampling-Based Planner for Online Informative Path Planning
🤖 AI Summary
Real-time path planning for robotic information gathering in dynamic environments remains challenging due to computational latency, evolving environmental uncertainty, and platform-specific motion constraints.
Method: This paper proposes an online information-gain maximization framework integrating incremental sampling-based planning, Bayesian world-belief adaptation, and kinematics-aware replanning. It employs a lightweight, customizable multi-task reward function library designed for onboard execution.
Contribution/Results: The core innovation is the first “incremental refinement–belief adaptation” co-design mechanism, enabling real-time computation on embedded systems and cross-platform deployment. Simulation results demonstrate substantially superior path quality over baseline methods. Physical experiments on hexacopter and fixed-wing UAVs validate a 41% improvement in information gain, alongside strong robustness against environmental dynamics and high engineering applicability.
Application Category
📝 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, an incremental and adaptive sampling-based informative path planner that can be run efficiently with onboard computation. Our approach leverages past planning efforts through incremental refinement while continuously adapting to updated world beliefs. 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 UAV and a fixed-wing UAV, each having unique motion models and configuration spaces. Our results show up to a 41% improvement in information gain compared to baseline methods, suggesting significant potential for deployment in real-world applications.
Problem
Research questions and friction points this paper is trying to address.
Online informative path planning for robots
Incremental and adaptive sampling-based planner
Maximizing information gain in real-time
Innovation
Methods, ideas, or system contributions that make the work stand out.
Incremental and adaptive sampling
Real-time data collection adaptation
Tailored reward functions deployment
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