全身运动规划与安全关键控制用于空中操作
Whole-body motion planning and safety-critical control for aerial manipulation
AI总结：
本文提出基于超级二次曲面的空中操作器全身运动规划与安全关键控制框架，通过融合Voronoi图与平衡流形生成平滑轨迹，并设计高阶控制屏障函数实现碰撞避让，实验验证其在复杂环境中的高效性和鲁棒性。
摘要：
空中操作结合多旋翼的机动性和机械臂的灵活性，以在复杂空间中执行复杂任务。然而，由于全身碰撞避让和常用几何抽象（如包围盒或椭球）的保守性，安全、动态可行的轨迹规划仍具挑战性。本文提出基于超级二次曲面（SQs）的空中操作器全身运动规划与安全关键控制框架。使用SQ-plus-proxy表示，我们以可微、几何精确的表面建模车辆和障碍物。利用此表示，我们引入最大间隙规划器，融合Voronoi图与平衡流形公式以生成平滑、碰撞感知的轨迹。我们进一步设计安全关键控制器，通过高阶控制屏障函数联合执行推力限制和碰撞避让。在仿真中，我们的方法在复杂环境中优于采样规划器，产生更快、更安全、更平滑的轨迹，并在几何保真度上超过基于椭球的基线。实际实验在物理空中操作平台上验证了可行性和鲁棒性，证明了在仿真和硬件设置中的一致性能。视频可在https://youtu.be/hQYKwrWf1Ak找到。
英文摘要：
Aerial manipulation combines the maneuverability of multirotors with the dexterity of robotic arms to perform complex tasks in cluttered spaces. Yet planning safe, dynamically feasible trajectories remains difficult due to whole-body collision avoidance and the conservativeness of common geometric abstractions such as bounding boxes or ellipsoids. We present a whole-body motion planning and safety-critical control framework for aerial manipulators built on superquadrics (SQs). Using an SQ-plus-proxy representation, we model both the vehicle and obstacles with differentiable, geometry-accurate surfaces. Leveraging this representation, we introduce a maximum-clearance planner that fuses Voronoi diagrams with an equilibrium-manifold formulation to generate smooth, collision-aware trajectories. We further design a safety-critical controller that jointly enforces thrust limits and collision avoidance via high-order control barrier functions. In simulation, our approach outperforms sampling-based planners in cluttered environments, producing faster, safer, and smoother trajectories and exceeding ellipsoid-based baselines in geometric fidelity. Actual experiments on a physical aerial-manipulation platform confirm feasibility and robustness, demonstrating consistent performance across simulation and hardware settings. The video can be found at https://youtu.be/hQYKwrWf1Ak.
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。
