AirSplan：杂乱3D高斯溅射环境中的四旋翼风险感知运动规划
AirSplan: Risk-Aware Motion Planning for Quadrotors in Cluttered 3D Gaussian Splats
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中文总结 AI 辅助
AirSplan提出一种结合归一化3D高斯溅射场景表示与基于可达性运动规划器的系统，利用微分平坦性计算紧致碰撞约束，在杂乱环境中实现高成功率（81.2%）的四旋翼无碰撞路径规划。
中文摘要 AI 辅助
四旋翼无人机越来越多地应用于农业、基础设施检查和维护等场景。在这些应用中，机器人必须在复杂的场景几何结构中导航，同时严格保持无碰撞。与地面领域不同，空中飞行器的轻微碰撞都可能导致机器人损毁。这一安全要求引出了一对技术挑战。首先，环境必须以足够的保真度进行表示，以编码复杂结构，即使在没有地面真实障碍物数据的情况下也是如此。其次，运动规划器必须利用这种表示来确定通往目标的无碰撞路径。本文提出了一种系统来解决这些互补的挑战。所提出的方法AirSplan采用了一种归一化的3D高斯溅射变体，该变体编码高保真场景几何。然后，它应用一种新颖的基于可达性的运动规划器，利用四旋翼的微分平坦性来计算连续时间碰撞约束，这些约束紧密地过度近似机器人的占用空间。实验表明，AirSplan在81.2%的具有挑战性的测试用例中成功找到路径，相比最近的基线方法的51.2%有显著提升。
英文摘要
Quadrotors are increasingly deployed in applications such as agriculture, infrastructure inspection, and maintenance. In each of these applications, the robot must navigate complex scene geometry while remaining strictly collision-free. Unlike in ground domains, even minor collisions for aerial vehicles can result in the loss of the robot. This safety requirement induces a pair of technical challenges. First, the environment must be represented with sufficient fidelity to encode complex structure, even when no ground-truth obstacle data is available. Second, a motion planner must leverage this representation to determine a collision-free path to the goal. This paper proposes a system that addresses these complementary challenges. The proposed method, AirSplan, adopts a normalized variant of 3D Gaussian Splatting that encodes high-fidelity scene geometry. It then applies a novel reachability-based motion planner that leverages the differential flatness of quadrotors to compute continuous-time collision constraints that tightly overapproximate the robot's occupancy. Experiments demonstrate that AirSplan successfully finds a path in 81.2% of challenging test cases, a significant improvement over the nearest baseline method's 51.2%.
发表机构
- University of Michigan（密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。
