Limited flight distance and time is a common problem for multicopters. We propose a method for finding the optimal speed and sideslip angle of a multicopter flying a given path to achieve either the longest flight distance or time. Since flight speed and sideslip are often free variables in multicopter path planning, they can be changed without changing the mission. The proposed method is based on a novel multivariable extremum seeking controller with adaptive step size, which is inspired by recent work from the machine learning community on stochastic optimization. Our method (a) does not require a power consumption model of the vehicle, (b) is computationally efficient and runs on low-cost embedded computers in real-time, and (c) converges faster than the standard extremum seeking controller with constant step size. We prove the stability of this approach and validate it through outdoor experiments. The method is shown to converge with different payloads and in the presence of wind. Compared to flying at the maximum achievable speed in the experiments with a uniformly selected random sideslip angle, flying at the optimal range speed and sideslip on average increases the flight range by 14.3% without payload and 19.4% with a box payload. In addition, compared to hovering, flying at the optimal endurance speed and sideslip increases the flight time by 7.5% without payload and 14.4% with a box payload. A video can be found at https://youtu.be/aLds8LVfogk

中文翻译：

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接收机对频谱管理的影响


飞行距离和时间有限是多旋翼飞行器的常见问题。我们提出了一种方法，用于找到飞行给定路径的多旋翼飞行器的最佳速度和侧滑角，以实现最长的飞行距离或时间。由于飞行速度和侧滑通常是多旋翼飞行器路径规划中的自由变量，因此可以在不改变任务的情况下改变它们。所提出的方法基于一种具有自适应步长的新型多变量极值搜索控制器，其灵感来自于机器学习社区关于随机优化的最新工作。我们的方法（a）不需要车辆的功耗模型，（b）计算效率高并且在低成本嵌入式计算机上实时运行，并且（c）比具有恒定步长的标准极值搜索控制器收敛得更快尺寸。我们证明了这种方法的稳定性，并通过室外实验进行了验证。该方法被证明可以在不同的有效载荷和有风的情况下收敛。与实验中以均匀选择的随机侧滑角的最大可实现速度飞行相比，以最佳航程速度和侧滑角飞行平均使空载飞行航程增加了14.3%，带箱式有效载荷飞行航程增加了19.4%。此外，与悬停相比，以最佳续航速度和侧滑飞行，无负载飞行时间增加了7.5%，带箱负载飞行时间增加了14.4%。视频可在 https://youtu.be/aLds8LVfogk 找到