OmniPlanner: Universal Exploration and Inspection Path Planning Across Robot Morphologies
cs.RO
Submitted: 2026-03-04
Updated: 2026-09-16
Comments: Accepted for publication in IEEE Transactions on Field Robotics (T-FR)
Code: https://github.com/ntnu-arl/gbplanner
Project page: https://ntnu-arl.github.io/omniplanner
License: http://creativecommons.org/licenses/by/4.0/
The gist: Autonomous robotic systems are increasingly deployed for mapping, monitoring, and inspection in complex and unstructured environments.
Terminology
Abstract
Autonomous robotic systems are increasingly deployed for mapping, monitoring, and inspection in complex and unstructured environments. However, most existing path planning approaches remain domain-specific (i.e., in the air, on land, or at sea), limiting their scalability and cross-platform applicability. This article presents OmniPlanner, a unified planning framework for autonomous exploration and inspection across aerial, ground, and underwater robots. The method integrates volumetric exploration and viewpoint-based inspection, alongside target reach behaviors within a single modular architecture, complemented by a platform abstraction layer that captures morphology-specific sensing, traversability and motion constraints. This enables the same planning strategy to generalize across distinct mobility domains with minimal retuning. The framework is validated through extensive simulation studies and field deployments in underground mines, industrial facilities, forests, submarine bunkers, and structured outdoor environments. Across these diverse scenarios, OmniPlanner demonstrates robust performance, consistent cross-domain generalization, and improved exploration and inspection efficiency compared to representative state-of-the-art baselines. Videos presenting the OmniPlanner framework and demonstrating its field deployments across aerial, ground, and underwater robots are available at https://ntnu-arl.github.io/omniplanner, and the source code is publicly available at https://github.com/ntnu-arl/gbplanner ros/tree/gbplanner3.
Sources
- REACT: Real-time Entanglement-Aware Coverage Path Planning for Tethered Underwater Vehicles
- GO-FEAP: Global Optimal UAV Planner Using Frontier-Omission-Aware Exploration and Altitude-Stratified Planning
- PG-LIO: Photometric-Geometric fusion for Robust LiDAR-Inertial Odometry
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