全向多旋翼无人机（omnicopter）平台的局部路径规划与避障
Local Path Planning and Obstacle Avoidance for an Omnicopter Platform
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中文总结 AI 辅助
本文针对全向多旋翼无人机，将动态窗口法扩展为6D-DWA并引入敏捷模式，实现了实时局部规划与避障，仿真验证其路径跟踪精度及避障性能良好。
中文摘要 AI 辅助
自主无人机（UAV）日益在杂乱环境中运行，此类环境下RRT*等全局规划器无法以控制速率直接部署。本文通过将动态窗口法（Dynamic Window Approach）扩展至6个自由度（6D-DWA），提出了一种适用于全向多旋翼无人机（omnicopter）的实时局部规划与避障模块。我们的方法通过三项措施实现实时可行性：（i）局部地图体素化；（ii）基于紧凑球体的无人机几何近似；（iii）6D搜索空间中的自适应速度采样。为提升对未知障碍物的反应性，我们引入了上下文感知的“敏捷模式”，该模式可在线调整评分权重，在避障机动中权衡目标进度、安全距离以及朝向约束。我们在仿真中对所提方法开展评估，涵盖计算压力测试、密集航路点路径跟踪以及静态/未知障碍物场景。结果显示，我们的规划器在0.2秒控制循环内始终稳定运行，跟踪航路点密集的全局路径时，平均横向跟踪误差小于0.1米，平均航向误差为13度，且能在静态环境中避免碰撞；针对未知障碍物避障，敏捷模式对偏心障碍物的成功率达79.3%，对中心障碍物的成功率为41.4%，这既凸显了自适应权重的有效性，也表明在高度受限几何场景中仍存在局限性。
英文摘要
Autonomous unmanned aerial vehicles (UAVs) increasingly operate in cluttered environments where global planners such as RRT* are not directly deployable at control rates. This paper presents a real-time local planning and obstacle avoidance module for an omnidirectional multirotor (omnicopter) by extending the Dynamic Window Approach to six degrees of freedom (6D-DWA). Our method achieves real-time feasibility through (i) local-map voxelisation, (ii) a compact sphere-based approximation of the vehicle geometry, and (iii) adaptive velocity sampling in the 6D search space. To improve reactivity to unknown obstacles, we introduce a context-aware "Agile Mode" that adjusts scoring weights online to trade-off between goal progress, clearance, and heading/facing constraints during evasive manoeuvres. We evaluate our approach in simulation across computational stress tests, dense-waypoint path tracking, and static/unknown obstacle scenarios. Our planner runs consistently within a 0.2s control loop, tracks waypoint-dense global paths with < 0.1m average cross-track error and 13deg average heading error, and avoids collisions in static environments. For unknown obstacle avoidance, Agile Mode achieves 79.3% success for an off-centre obstacle and 41.4% for a centred obstacle, highlighting both the effectiveness of adaptive weighting and remaining limitations in highly constrained geometries.
发表机构
- Faculty of Aerospace Engineering, Delft University of Technology（代尔夫特理工大学航空航天工程学院）
- Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
- Center for Artificial Intelligence and Robotics, New York University Abu Dhabi（纽约大学阿布扎比分校人工智能与机器人中心）
机构由 AI 辅助整理，请以论文原文为准。
