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.
翻译:有限的飞行距离和时间是多开天花板的常见问题。 我们建议了一种方法, 找到多开天花地机最佳速度和侧侧翻角度, 以达到最长的飞行距离或时间。 由于在多开天花地机路径规划中, 飞行速度和侧翻页通常是自由变量, 可以在不改变任务的情况下改变。 所提议方法基于一个创新的多变量外红膜搜索控制器, 其适应性步骤大小是机器学习界最近的工作所启发的。 我们的方法 (a) 不需要车辆的电动消费模型, (b) 是计算效率高的, 运行在成本低的嵌入式计算机上实现最长的飞行速度。 (c) 飞行速度和侧翻开速度通常比标准的极限控制器快速。 我们证明这个方法的稳定性, 并通过户外实验验证它。 与不同的有效载载荷和风力相趋近。 相比, 在实验中, 以统一选择的随机侧翻动角度角度飞行, 以最佳射程速度和侧翻转平平均飞行距离速度将飞行距离范围提高14. 将飞行范围范围提高14.3%, 将飞行速度提高到升升升升至19.4, 上, 将飞行速度比为最高方向, 将飞行速度递升升升升升升为飞行速度为最高为14: 上,, 将飞行速度为最高速度为飞行速度为飞行速度,, 升为飞行速度为飞行速度为飞行速度为14.