Autonomous Navigation for Object Capture with Multicopters

使用多旋翼飞行器进行物体捕捉的自主导航

• 批准号: 200548633
• 负责人: Professor Dr. Sven Behnke
• 金额: --
• 依托单位: Lehrstuhl für Geodäsie
• 依托单位国家: 德国
• 项目类别: Research Units
• 财政年份: 2011
• 资助国家: 德国
• 起止时间: 2010-12-31 至 2018-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/200548633?language=en
• 关键词: Autonomous; Navigation; Object; Capture; Multicopters

Objective of the proposed project is the development of new methods for the autonomous navigation of multicopters in sub-urban areas. Starting from the results of the first funding period, advanced copter capabilities will be developed. Allocentric mapping onboard the copter (in P2) makes it possible to also plan allocentric navigation onboard. The detection of objects in P2 allows for navigation relative to these. The perception of dynamic obstacles (in P2) will be the basis for anticipatory planning. Start and landing will be made automatic. Navigation goals are pursued on different time scales. Task of the slow mission planning is the creation of a flight mission based on the requirements of the user. The result is a sequence of posed at which the main sensor, a high-resolution camera, shall capture images. When executing the mission, 3D navigation paths are planned with medium frequency from the current pose of the copter (from P1) to the next view pose. The planner will optimize multiple criteria simultaneously: the avoidance of obstacles, the maintenance of communication and localization, wind strength, and control costs. Based on the egocentric obstacle map created in P2, a local planner will generate with high rate 3D obstacle-avoiding paths. In this way, the copter will be able to react quickly on external disturbances, in particular wind and obstacle detections in its vicinity. In order to plan with high rates with the limited computational resources of the onboard PC, multi-resolution methods will be advanced. In addition to the spatial discretization, multiresolution shall be used in the time dimension for planning with moving obstacles. For the relative navigation under consideration of the copter flight dynamics, fast model predictive control shall be accelerated by multiresolution techniques. We also aim at robustness against the loss of sub-systems, i.e. of sensors, communication, or motors. For all of these cases, we will develop suitable behaviors to overcome the loss (e.g. fight into an area of direct sight to the base station or GNSS satellites) or at least allow for a safe landing. Finally, we aim at learning navigation strategies from human experts and their transfer to novel situations. To this end, we will learn cost functions with inverse reinforcement learning methods.

拟议项目的目标是开发郊区多旋翼机自主导航的新方法。从第一个资助期的结果开始，将开发先进的直升机能力。直升机上的非中心映射（在P2中）使得也可以在飞机上规划非中心导航。P2中的对象的检测允许相对于这些对象的导航。对动态障碍的感知（在P2中）将是预期规划的基础。启动和着陆将自动进行。在不同的时间尺度上追求导航目标。慢速使命规划的任务是根据用户的要求创建飞行使命。其结果是一系列的姿势，在该姿势下，主传感器（高分辨率相机）将捕获图像。当执行使命时，以中等频率规划从直升机的当前姿态（从P1）到下一视图姿态的3D导航路径。规划者将同时优化多个标准：避开障碍物，保持通信和定位，风力强度和控制成本。基于在P2中创建的以自我为中心的障碍物地图，局部规划器将以高速率生成3D避障路径。通过这种方式，直升机将能够对外部干扰做出快速反应，特别是在其附近的风和障碍物检测。为了利用机载PC有限的计算资源进行高速率规划，将提出多分辨率方法。除了空间离散化之外，还应在时间维度中使用多分辨率，以规划移动障碍物。对于考虑直升机飞行动力学的相对导航，需要采用多分辨率技术来加速快速模型预测控制。我们的目标还包括子系统的鲁棒性，即传感器，通信或电机的损失。对于所有这些情况，我们将制定适当的行为来克服损失（例如，进入基站或GNSS卫星的直接视线区域）或至少允许安全着陆。最后，我们的目标是从人类专家那里学习导航策略，并将其转移到新的情况下。为此，我们将使用逆强化学习方法学习成本函数。

项目成果

Autonomous Navigation for Micro Aerial Vehicles in Complex GNSS-denied Environments

复杂 GNSS 拒绝环境中微型飞行器的自主导航

• DOI: 10.1007/s10846-015-0274-3
• 发表时间: 2016
• 期刊: Journal of Intelligent & Robotic Systems
• 影响因子: 3.3
• 作者: Nieuwenhuisen;Droeschel;Behnke
• 通讯作者: Behnke

Fast Time-optimal Avoidance of Moving Obstacles for High-Speed MAV Flight

高速 MAV 飞行时快速、最佳时间避开移动障碍物

• DOI: 10.1109/iros40897.2019.8968103
• 发表时间: 2019
• 期刊: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: Behnke

Fast Autonomous Flight in Warehouses for Inventory Applications

• DOI: 10.1109/lra.2018.2849833
• 发表时间: 2018-10-01
• 期刊: IEEE ROBOTICS AND AUTOMATION LETTERS
• 影响因子: 5.2
• 作者: Beul, Marius;Droeschel, David;Behnke, Sven
• 通讯作者: Behnke, Sven

Local multiresolution trajectory optimization for micro aerial vehicles employing continuous curvature transitions

采用连续曲率过渡的微型飞行器的局部多分辨率轨迹优化

• DOI: 10.1109/iros.2016.7759497
• 发表时间: 2016
• 期刊: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: Nieuwenhuisen;Behnke
• 通讯作者: Behnke

Autonomous MAV-based Indoor Chimney Inspection with 3D Laser Localization and Textured Surface Reconstruction

• DOI: 10.1007/s10846-018-0791-y
• 发表时间: 2019-02-01
• 期刊: JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
• 影响因子: 3.3
• 作者: Quenzel, Jan;Nieuwenhuisen, Matthias;Behnke, Sven
• 通讯作者: Behnke, Sven