arXiv 2410.050792024-10-07cs.RO

HE-Nav：面向杂乱环境中空地机器人的高性能高效导航系统

HE-Nav: A High-Performance and Efficient Navigation System for Aerial-Ground Robots in Cluttered Environments

University of Hong Kong（香港大学）；Peng Cheng Laboratory（鹏城实验室）；Huazhong University of Science and Technology（华中科技大学）

Junming Wang, Zekai Sun, Xiuxian Guan, Tianxiang Shen, Dong Huang, Zongyuan Zhang, Tianyang Duan, Fangming Liu, Heming Cui

备注：: Accepted to IEEE RA-L

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AI总结：

针对现有空地机器人导航系统在严重遮挡杂乱环境中性能与效率不足的问题，提出HE-Nav系统，通过轻量感知网络与节能规划器实现高效导航，能耗降为1/7且仿真规划成功率达98%。

摘要：

现有的空地机器人（AGR）导航系统通过采用3D语义场景补全网络进行体素占用预测，并构建欧几里得符号距离场（ESDF）地图用于无碰撞路径规划，已在轻度遮挡场景（如建筑物）中取得进展。然而，由于感知网络预测精度低、路径规划器计算开销大带来的局限性，这些系统在存在严重遮挡的杂乱环境（如茂密森林或高墙）中表现出次优的性能与效率。本文提出HE-Nav，这是首个专为在杂乱环境中作业的AGR定制的高性能高效导航系统。其感知模块采用轻量级语义场景补全网络（LBSCNet），以鸟瞰图（BEV）特征融合为引导，并通过精心设计的SCB-Fusion模块与注意力机制增强，可在杂乱区域实时高效地进行障碍物预测，生成完整的局部地图。基于该补全地图，我们提出的新型AG-Planner采用高能效的kinodynamic A*搜索算法，确保规划过程节能。后续的轨迹优化流程可生成安全、平滑、动态可行且无需依赖ESDF的空地混合路径。大量实验表明，HE-Nav在真实场景中实现了7倍的能耗节省，同时在仿真场景中保持98%的规划成功率。代码与视频可在我们的项目页面获取：https://jmwang0117.github.io/HE-Nav/。

英文摘要：

Existing AGR navigation systems have advanced in lightly occluded scenarios (e.g., buildings) by employing 3D semantic scene completion networks for voxel occupancy prediction and constructing Euclidean Signed Distance Field (ESDF) maps for collision-free path planning. However, these systems exhibit suboptimal performance and efficiency in cluttered environments with severe occlusions (e.g., dense forests or tall walls), due to limitations arising from perception networks' low prediction accuracy and path planners' high computational overhead. In this paper, we present HE-Nav, the first high-performance and efficient navigation system tailored for AGRs operating in cluttered environments. The perception module utilizes a lightweight semantic scene completion network (LBSCNet), guided by a bird's eye view (BEV) feature fusion and enhanced by an exquisitely designed SCB-Fusion module and attention mechanism. This enables real-time and efficient obstacle prediction in cluttered areas, generating a complete local map. Building upon this completed map, our novel AG-Planner employs the energy-efficient kinodynamic A* search algorithm to guarantee planning is energy-saving. Subsequent trajectory optimization processes yield safe, smooth, dynamically feasible and ESDF-free aerial-ground hybrid paths. Extensive experiments demonstrate that HE-Nav achieved 7x energy savings in real-world situations while maintaining planning success rates of 98% in simulation scenarios. Code and video are available on our project page: https://jmwang0117.github.io/HE-Nav/.

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。