OmniPlanner: Universal Exploration and Inspection Path Planning across Robot Morphologies

2026-03-04

AI Summary

This work proposes OmniPlanner, a unified cross-domain path planning framework that overcomes the limitations of existing methods, which are typically confined to specific robot morphologies—such as aerial, ground, or underwater platforms—and struggle to generalize across complex, unstructured environments. By integrating voxelized environmental representation, viewpoint optimization, and goal-directed navigation within a modular architecture featuring a morphology-agnostic platform abstraction layer, OmniPlanner effectively accommodates diverse robotic perception, traversability, and motion constraints. For the first time, it enables a single planning strategy to support universal exploration and inspection tasks across aerial, ground, and underwater robots without re-tuning parameters. Evaluated in diverse real-world scenarios—including mines, industrial facilities, forests, and underwater structures—OmniPlanner demonstrates significant improvements over state-of-the-art approaches in exploration efficiency, inspection quality, and cross-domain generalization.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Multiagent Planning

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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., either on air, land, or 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.

Problem

• path planning
• cross-domain generalization
• robot morphologies
• autonomous exploration
• inspection

Innovation

• universal path planning
• cross-morphology autonomy
• modular planning architecture
• platform abstraction layer
• volumetric exploration