发表机构
UC San Diego; Shield AI（加州大学圣地亚哥分校; 护盾人工智能公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对无人机在杂乱环境中的自主飞行，提出围绕带符号距离函数协同设计映射与规划阶段。开发OREN重建SDF，Bubble$^\star$基于距离信息规划，减少碰撞检查。集成方法在四旋翼飞行器上实时导航，提升SDF估计并快速找到长轨迹。
AI 中文摘要
在杂乱环境中的自主飞行要求机器人在机载且实时的情况下构建周围环境的几何地图并规划安全、动态可行的轨迹。传统方法将映射和规划视为分开的阶段，且常依赖二进制占用进行碰撞检查。本文认为这两个阶段应围绕单一表示——带符号距离函数（SDF）进行协同设计。通过对到最近障碍物的距离进行编码，SDF为规划和轨迹优化提供比单独占用更丰富的信息。我们开发了八叉树残差网络（OREN），它将显式八叉树先验与隐式神经残差配对，以从点云观测中在线重建SDF，兼具体素方法的效率和神经方法的准确性及可微性。同时，我们开发了Bubble$^\star$，一种基于搜索的规划器，利用距离信息生长最大无碰撞球（即气泡），具有终止、完备性和故障检测的形式保证。在气泡图上进行规划与基于网格的A$^\star$搜索相比显著减少碰撞检查，并返回形成安全走廊的气泡序列用于轨迹优化。我们在四旋翼飞行器上演示了集成的OREN - Bubble$^\star$方法，在严格计算约束下实时导航未见的室内环境。OREN与基线相比将SDF估计提高了22%，而Bubble$^\star$在1 - 3秒内找到穿越杂乱环境约90米的轨迹，而基线在相同环境中最多需10秒。
英文摘要
Autonomous flight in cluttered environments requires a robot to build a geometric map of its surroundings and plan safe, dynamically feasible trajectories, all onboard and in real time. Conventional approaches treat mapping and planning as separate stages and often rely on binary occupancy for collision checking. We argue that these two stages should be co-designed around a single representation: a signed distance function (SDF). By encoding distance to the nearest obstacle, an SDF provides richer information for planning and trajectory optimization than occupancy alone. We develop an Octree REsidual Network (OREN) that pairs an explicit octree prior with an implicit neural residual to reconstruct SDFs online from point cloud observations with the efficiency of volumetric methods and the accuracy and differentiability of neural methods. In tandem, we develop Bubble$^\star$, a search-based planner that exploits the distance information to grow maximal collision-free balls, which we call bubbles, with formal guarantees of termination, completeness, and failure detection. Planning over a graph of bubbles significantly reduces collision checks compared to a grid-based A$^\star$ search and returns a bubble sequence that forms a safe corridor for trajectory optimization. We demonstrate the integrated OREN-Bubble$^\star$ approach onboard a quadrotor, navigating unseen indoor environments in real time under tight compute constraints. OREN improves SDF estimation by $22$% compared to baselines, while Bubble$^\star$ finds trajectories spanning $\approx 90$ m through a cluttered environment in $1$-$3$ sec., whereas baselines take up to $10$ sec. in the same environment.
Comments25 pages, 10 figures, 5 tables
