如果给穿越机使用强大的CPU，结合人工智能，实现穿越机自主飞行，绕障，定点飞行，跟踪动点飞行，自主花飞，这将会是一个很大的市场！
因此，穿越机在以后自主飞行时将大放异彩。
首先，实现自主飞行，定点航线飞行，这个已经被INAV或者Ardupilot实现了。
但是，这种自主飞行，更多的像是航拍机一样飞行，并没有特别多的动作。需要事先在CPU里规划动作，并且实时修正（比如遇到风，障碍物，干扰，水（湿度变大），其他穿越机，动态障碍物，数据波动等等），这就需要非常强大的算力。
谁如果能做到在理想环境下实现穿越机机动性飞行，那也将是非常不错的。
搜索了一下论文，目前看应该是0篇。
国外搜到了一个研究，链接报道如下：
这个实现了室内的自主竞速飞行，不清楚是如何实现的。需要了解一下。
总之，有小组在验证这个是可以实现的。
资料：
B站视频：AI技术控制穿越机， UZH苏黎世大学团队成果展示，这速度太残暴了！_哔哩哔哩_bilibili
B站视频：【UZH Robotics】深度学习算法对穿越机控制方法_哔哩哔哩_bilibili YouTube的原视频： Deep Drone Acrobatics (RSS 2020 Video Pitch) – YouTube
原片发布于2020年7月22日。距今已经3年多。
Performing acrobatic maneuvers with quadrotors is extremely challenging. Acrobatic flight requires high thrust and extreme angular accelerations that push the platform to its physical limits. Professional drone pilots often measure their level of mastery by flying such maneuvers in competitions. In this work, we propose to learn a sensorimotor policy that enables an autonomous quadrotor to fly extreme acrobatic maneuvers with only onboard sensing and computation. We train the policy entirely in simulation by leveraging demonstrations from an optimal controller that has access to privileged information. We use appropriate abstractions of the visual input to enable transfer to a real quadrotor. We show that the resulting policy can be directly deployed in the physical world without any fine-tuning on real data. Our methodology has several favorable properties: it does not require a human expert to provide demonstrations, it cannot harm the physical system during training, and it can be used to learn maneuvers that are challenging even for the best human pilots. Our approach enables a physical quadrotor to fly maneuvers such as the Power Loop, the Barrel Roll, and the Matty Flip, during which it incurs accelerations of up to 3g.
PDF: http://rpg.ifi.uzh.ch/docs/RSS20_Kauf…
For more information about our research, visit these pages: 1. Vision-based quadrotor flight: http://rpg.ifi.uzh.ch/research_mav.html 2. Drone Racing: http://rpg.ifi.uzh.ch/research_drone_… 3. Aggressive flight: http://rpg.ifi.uzh.ch/aggressive_flig… 4. Deep Learning: http://rpg.ifi.uzh.ch/research_learni… Affiliations: E. Kaufmann, A. Loquercio and D. Scaramuzza are with the Robotics and Perception Group, Dep. of Informatics, University of Zurich, and Dep. of Neuroinformatics, University of Zurich and ETH Zurich, Switzerland http://rpg.ifi.uzh.ch/ R. Ranftl, M. Müller and V. Koltun are with Intel Labs http://vladlen.info/
