OmniPlanner: 跨机器人形态的通用探索与检查路径规划
OmniPlanner: Universal Exploration and Inspection Path Planning Across Robot Morphologies
AI总结：
OmniPlanner是一种跨机器人形态的通用路径规划框架，通过统一架构实现探索与检查任务，适用于空中、地面和水下环境，提升了跨领域的适应性和效率。
摘要：
自主机器人系统正越来越多地用于复杂和非结构化环境中的制图、监控和检查。然而，大多数现有路径规划方法仍具有领域特定性（即仅限于空中、陆地或水下），限制了它们的可扩展性和跨平台适用性。本文提出了OmniPlanner，一种用于自主探索和检查的统一规划框架，适用于空中、地面和水下机器人。该方法在单一模块化架构中集成了体积探索和基于视角的检查，以及目标可达行为，辅以一个平台抽象层，该层捕捉形态特定的感知、可通行性和运动约束。这使得相同规划策略能够跨不同移动领域进行最小调整。该框架通过广泛的模拟研究和地下矿井、工业设施、森林、潜艇基地和结构化户外环境中的现场部署进行了验证。在这些多样的场景中，OmniPlanner展示了稳健的性能，一致的跨领域泛化能力以及优于代表性最先进的基线的探索和检查效率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
