Abstract
Limited flight distance and time is a common problem for multicopters. We propose a method for finding the optimal speed and heading of multicopters while flying a given path to achieve the longest flight distance or time. Since flight speed and heading 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. It (a) does not require any power consumption model of the vehicle, (b) can be executed online, (c) is computationally efficient and runs on low-cost embedded computers in real-time, and (d) converges faster than the standard extremum seeking controller with constant step size. We prove the stability of this proposed extremum seeking controller, and conduct outdoor experiments to validate the effectiveness of this method with different initial conditions, with and without payload. This method could be especially useful for applications such as package delivery, where the weight, size and shape of the payload vary between deliveries and the power consumption of the vehicle is hard to model. Experiments show that compared to flying at the maximum speed with a bad heading angle, flying at the optimal range speed and heading reduces the energy consumed per distance by 24.9% without payload and 33.5% with a box payload. In addition, compared to hovering, flying at the optimal endurance speed and heading reduces the the power consumption by 7.0% without payload and 12.6% with a box payload.
Abstract (translated)
URL
https://arxiv.org/abs/2108.03807
