基于深度传感器的混乱环境下的鲁棒自主飞行

IF 1.5 4区 工程技术 Q2 ENGINEERING, AEROSPACE
Liang Lu, Alexander Yunda, Adrian Carrio, P. Campoy
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引用次数: 4

摘要

本文提出了一种新型的无人机无碰撞导航系统,该系统基于点云,与基线方法相比性能更好,能够在杂乱的环境中进行高速飞行,如森林或许多室内工业工厂。该算法从物理传感器(如激光雷达、深度相机)获取点云信息,然后使用Voxblox将其转换为占用地图,然后由快速探索随机树使用该地图生成有限的候选路径。使用改进的运动规划目标函数的协方差哈密顿优化来选择最佳候选轨迹并对其进行更新。最后,生成最佳候选轨迹,并将其发送到模型预测控制控制器。在四种不同的仿真环境中对所提出的导航策略进行了评估;结果表明,与基线方法相比,该方法具有更好的成功率和更短的目标到达距离。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust autonomous flight in cluttered environment using a depth sensor
This paper presents a novel collision-free navigation system for the unmanned aerial vehicle based on point clouds that outperform compared to baseline methods, enabling high-speed flights in cluttered environments, such as forests or many indoor industrial plants. The algorithm takes the point cloud information from physical sensors (e.g. lidar, depth camera) and then converts it to an occupied map using Voxblox, which is then used by a rapid-exploring random tree to generate finite path candidates. A modified Covariant Hamiltonian Optimization for Motion Planning objective function is used to select the best candidate and update it. Finally, the best candidate trajectory is generated and sent to a Model Predictive Control controller. The proposed navigation strategy is evaluated in four different simulation environments; the results show that the proposed method has a better success rate and a shorter goal-reaching distance than the baseline method.
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来源期刊
CiteScore
3.00
自引率
7.10%
发文量
13
审稿时长
>12 weeks
期刊介绍: The role of the International Journal of Micro Air Vehicles is to provide the scientific and engineering community with a peer-reviewed open access journal dedicated to publishing high-quality technical articles summarizing both fundamental and applied research in the area of micro air vehicles.
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