统一规划空中、地面、水下机器人的探索路径，一套方法适配多种形态。
OmniPlanner: Universal Exploration and Inspection Path Planning across Robot Morphologies
- 模块化架构融合三维探索与视角检查，支持多形态机器人
- 跨域通用性强，无需重新调参即可在不同环境稳定运行
- 适用于矿山、工业场、森林等复杂场景，效率优于现有方法
自主机器人系统在复杂非结构化环境中越来越多地用于测绘、监测和巡检。然而，现有路径规划方法大多局限于特定领域（如空中、陆地或水域），限制了其可扩展性和跨平台适用性。本文提出OmniPlanner，一种面向空中、地面和水下机器人的统一探索与巡检规划框架。该方法在单一模块化架构中集成体素化探索、基于视角的检查以及目标可达行为，并通过平台抽象层捕捉形态特异性感知、通行性和运动约束。这使得同一规划策略能在不同移动领域间有效泛化，仅需少量调参。框架在地下矿井、工业设施、森林、水下掩体及结构化户外环境等场景中进行了广泛的仿真与实地部署验证。在多种复杂场景下，OmniPlanner展现出稳健性能、一致的跨域泛化能力，以及相比代表性先进基线更高的探索与巡检效率。
原文摘要 · Abstract (English)
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.
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