# An informative path planning framework for UAV-based terrain monitoring

arXiv: https://arxiv.org/abs/1809.03870 · HTML: https://mlsanity.com/paper/1809.03870/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2018.

## Built on by (most cited first, 24 of 24)

- [Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing](https://mlsanity.com/paper/2109.13570/) (2021)
- [GOMAA-Geo: GOal Modality Agnostic Active Geo-localization](https://mlsanity.com/paper/2406.01917/) (2024)
- [Adaptive Path Planning for UAVs for Multi-Resolution Semantic Segmentation](https://mlsanity.com/paper/2203.01642/) (2022)
- [An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping](https://mlsanity.com/paper/2302.03347/) (2023)
- [SC-Explorer: Incremental 3D Scene Completion for Safe and Efficient Exploration Mapping and Planning](https://mlsanity.com/paper/2208.08307/) (2022)
- [Deep Reinforcement Multi-agent Learning framework for Information Gathering with Local Gaussian Processes for Water Monitoring](https://mlsanity.com/paper/2401.04631/) (2024)
- [Deep Reinforcement Learning with Dynamic Graphs for Adaptive Informative Path Planning](https://mlsanity.com/paper/2402.04894/) (2024)
- [Semi-Supervised Active Learning for Semantic Segmentation in Unknown Environments Using Informative Path Planning](https://mlsanity.com/paper/2312.04402/) (2023)
- [Aerial View Localization with Reinforcement Learning: Towards Emulating Search-and-Rescue](https://mlsanity.com/paper/2209.03694/) (2022)
- [Sequential Bayesian Optimization for Adaptive Informative Path Planning with Multimodal Sensing](https://mlsanity.com/paper/2209.07660/) (2022)
- [Censored Deep Reinforcement Patrolling with Information Criterion for Monitoring Large Water Resources using Autonomous Surface Vehicles](https://mlsanity.com/paper/2210.08115/) (2022)
- [Adaptive Path Planning for UAV-based Multi-Resolution Semantic Segmentation](https://mlsanity.com/paper/2108.01884/) (2021)
- [Decentralized Multi-Agent Active Search and Tracking when Targets Outnumber Agents](https://mlsanity.com/paper/2401.03154/) (2024)
- [Variable Time-Step MPC for Agile Multi-Rotor UAV Interception of Dynamic Targets](https://mlsanity.com/paper/2503.14184/) (2025)
- [OffRIPP: Offline RL-based Informative Path Planning](https://mlsanity.com/paper/2409.16830/) (2024)
- [Towards Robust Multi-UAV Collaboration: MARL with Noise-Resilient Communication and Attention Mechanisms](https://mlsanity.com/paper/2503.02913/) (2025)
- [Bayesian-Driven Graph Reasoning for Active Radio Map Construction](https://mlsanity.com/paper/2508.09142/) (2025)
- [Informative Planning for Worst-Case Error Minimisation in Sparse Gaussian Process Regression](https://mlsanity.com/paper/2203.03828/) (2022)
- [Active search and coverage using point-cloud reinforcement learning](https://mlsanity.com/paper/2312.11410/) (2023)
- [DyPNIPP: Predicting Environment Dynamics for RL-based Robust Informative Path Planning](https://mlsanity.com/paper/2410.17186/) (2024)
- [Beyond Task Performance: A Metric-Based Analysis of Sequential Cooperation in Heterogeneous Multi-Agent Destructive Foraging](https://mlsanity.com/paper/2602.10685/) (2026)
- [DiffVAS: Diffusion-Guided Visual Active Search in Partially Observable Environments](https://mlsanity.com/paper/2605.15519/) (2026)
- [Autonomous UAV Route Planning for Coverage Maximization in Environmental Monitoring: A Systematic Literature Review](https://mlsanity.com/paper/2607.13054/) (2026)
- [Expected Free Energy-based Informative Path Planning for Robotic Mars Exploration](https://mlsanity.com/paper/2608.14466/) (2026)

## Abstract (arXiv)

Unmanned Aerial Vehicles (UAVs) represent a new frontier in a wide range of monitoring and research applications. To fully leverage their potential, a key challenge is planning missions for efficient data acquisition in complex environments. To address this issue, this article introduces a general Informative Path Planning (IPP) framework for monitoring scenarios using an aerial robot, focusing on problems in which the value of sensor information is unevenly distributed in a target area and unknown a priori . The approach is capable of learning and focusing on regions of interest via adaptation to map either discrete or continuous variables on the terrain using variable-resolution data received from probabilistic sensors. During a mission, the terrain maps built online are used to plan information-rich trajectories in continuous 3-D space by optimizing initial solutions obtained by a coarse grid search. Extensive simulations show that our approach is more efficient than existing methods. We also demonstrate its real-time application on a photorealistic mapping scenario using a publicly available dataset and demonstrate a proof of concept for an agricultural monitoring task.