Autonomous Navigation for Object Capture with Multicopters

点击翻译按钮获取中文摘要

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.