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.

翻译：自主机器人系统在复杂和非结构化环境中进行测绘、监测与巡检的应用日益广泛。然而，现有的大多数路径规划方法仍局限于特定领域（即空中、地面或水下），限制了其可扩展性和跨平台适用性。本文提出了OmniPlanner，一个适用于空中、地面和水下机器人的统一自主探索与巡检规划框架。该方法将体积探索、基于视点的巡检以及目标抵达行为集成在一个单一的模块化架构中，并辅以一个平台抽象层，该抽象层捕获了与形态相关的感知、可通行性与运动约束。这使得同一规划策略能够以最小的重新调整，泛化到不同的运动域。该框架通过广泛的仿真研究以及在地下矿井、工业设施、森林、潜艇掩体和结构化室外环境中的实地部署得到了验证。在这些多样化的场景中，与代表性的先进基线方法相比，OmniPlanner展现出鲁棒的性能、一致的跨领域泛化能力以及更高的探索与巡检效率。