空地协同中用于四旋翼控制的无全局状态避障方法
Global-State-Free Obstacle Avoidance for Quadrotor Control in Air-Ground Cooperation
AI总结：
针对空地协同中CoNi-MPC框架缺乏环境信息难以避障的问题，提出无需全局状态的CoNi-OA算法，仅用单帧LiDAR数据生成调制矩阵调速避障，计算快且适配静动态未知场景。
摘要：
CoNi-MPC（协同非惯性系模型预测控制）仅依赖相对状态，无需全局状态估计，为空地协同任务中的无人机控制提供了高效框架。然而，其缺乏环境信息的特点给避障带来了重大挑战。为解决这一问题，我们提出了一种新型避障算法——基于协同非惯性系的避障算法（Cooperative Non-inertial frame-based Obstacle Avoidance，CoNi-OA），该算法专门针对无人机-无人地面车辆（UAV-UGV）协同场景设计，无需依赖全局状态估计或障碍物预测。CoNi-OA的独特之处在于利用无人机采集的单帧原始LiDAR（激光雷达）数据生成调制矩阵，直接调整四旋翼的速度以实现避障。这种基于调制的方法能够在UGV的非惯性系内实时生成无碰撞轨迹，大幅降低计算需求（每次迭代耗时不足5毫秒），同时在动态且不可预测的环境中保障安全性。本研究的核心贡献包括：（1）提出一种基于调制的避障算法，专门适配无全局状态的非惯性系下UAV-UGV协同场景；（2）仅基于单帧LiDAR数据实现快速实时轨迹生成，无需障碍物建模或预测；（3）对静态和动态环境均具备适应性，从而拓展了在无特征或未知场景中的适用性。
英文摘要：
CoNi-MPC provides an efficient framework for UAV control in air-ground cooperative tasks by relying exclusively on relative states, eliminating the need for global state estimation. However, its lack of environmental information poses significant challenges for obstacle avoidance. To address this issue, we propose a novel obstacle avoidance algorithm, Cooperative Non-inertial frame-based Obstacle Avoidance (CoNi-OA), designed explicitly for UAV-UGV cooperative scenarios without reliance on global state estimation or obstacle prediction. CoNi-OA uniquely utilizes a single frame of raw LiDAR data from the UAV to generate a modulation matrix, which directly adjusts the quadrotor's velocity to achieve obstacle avoidance. This modulation-based method enables real-time generation of collision-free trajectories within the UGV's non-inertial frame, significantly reducing computational demands (less than 5 ms per iteration) while maintaining safety in dynamic and unpredictable environments. The key contributions of this work include: (1) a modulation-based obstacle avoidance algorithm specifically tailored for UAV-UGV cooperation in non-inertial frames without global states; (2) rapid, real-time trajectory generation based solely on single-frame LiDAR data, removing the need for obstacle modeling or prediction; and (3) adaptability to both static and dynamic environments, thus extending applicability to featureless or unknown scenarios.
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。
