Informative Path Planning

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Overview

Informative path planning studies how robots should move and sense to reduce uncertainty, find targets, build maps, or monitor changing environments. Common approaches score routes using mutual information, entropy reduction, Gaussian-process beliefs, or Fisher information, then optimize them with receding-horizon search, trajectory optimization, or multi-robot coordination. Recent methods also incorporate semantic beliefs, learned planners, communication limits, and physical constraints such as energy, sampling load, and hazard exposure. A central challenge is balancing information value against computation and mission cost, especially when beliefs are poorly calibrated, observations overlap, or multiple robots must coordinate.

Latest papers

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36 papers5 papers in the last four weeks, up 67% on the four weeks before. 0.0% of all new papers.

  1. Hierarchical Multi-Modal Planning for Fixed-Altitude Sparse Target Search and Sampling

    Mar 9, 2026Lingpeng Chen, Yuchen Zheng, Apple Pui-Yi Chui +2Informative Path PlanningUnderwater Robotics

  2. Multi-Agent Off-World Exploration for Sparse Evidence Discovery via Gaussian Belief Mapping and Dual-Domain Coverage

    Mar 8, 2026Zhuoran Qiao, Tianxin Hu, Thien-Minh Nguyen +1Multi-Robot ExplorationInformative Path Planning

    National University of Singapore, Singapore · Nanyang Technological University, Singapore · The University of Queensland, Australia

  3. LIPP: Load-Aware Informative Path Planning with Physical Sampling

    Mar 6, 2026Hojune Kim, Guangyao Shi, Gaurav S. SukhatmeInformative Path PlanningConstrained Optimization

    School of Electrical and Computer Engineering University of Southern California Los Angeles, California · School of Computer Science University of Southern California Los Angeles, California

  4. Navigating in Uncertain Environments with Heterogeneous Visibility

    Mar 3, 2026Jongann Lee, Melkior OrnikRobot NavigationInformative Path Planning

    University of Illinois Urbana-Champaign (UIUC), Urbana, IL 61801, USA.

  5. Homotopic information gain for sparse active target tracking

    Feb 20, 2026Jennifer Wakulicz, Ki Myung Brian Lee, Teresa Vidal-Calleja +1Informative Path PlanningMultimodal Trajectory Prediction

    Australian Centre for Robotics, School of Aerospace, Mechanical and Mechatronic Engineering, University of Sydney, Camperdown, 2006, NSW, Australia · Department of Electrical and Computer Engineering, UC San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0411, USA · School of Mechanical and Mechatronics Engineering, University of Technology Sydney, Ultimo, 2007, NSW, Australia

  6. Schur-MI: Fast Mutual Information for Robotic Information Gathering

    Feb 12, 2026Kalvik Jakkala, Jason O'Kane, Srinivas AkellaRobotic Data CollectionInformative Path Planning

    Texas A&M University · University of North Carolina at Charlotte