Autonomous Unmanned Aerial Vehicle (UAV) landing in windy conditions with MAP-Elites

Abstract

With the recent increase in the use of Unmanned Aerial Vehicles (UAVs) comes a surge of inexperienced aviators who may not have the requisite skills to react appropriately if weather conditions quickly change while their aircraft are in flight. This creates a dangerous situation, in which the pilot cannot safely land the vehicle. In this work we examine the use of the MAP-Elites algorithm to search for sets of weights for use in an artificial neural network. This neural network directly controls the thrust and pitching torque of a simulated 3-degree of freedom (2 linear, 1 rotational) fixed-wing UAV, with the goal of obtaining a smooth landing profile. We then examine the use of the same algorithm in high-wind conditions, with gusts up to 30 knots.Our results show that MAP-Elites is an effective method for searching for control policies, and by evolving two separate controllers and switching which controller is active when the UAV is near-ground level, we can produce a wider variety of phenotypic behaviors. The best controllers achieved landing at a vertical speed of <1 m s −1 and at an angle of approach of <1°degree.

Autonomous Landing of an UAV Using H∞ Based Model Predictive Control

“…It is usually done to reduce the costs and complexity of the system and mitigate risks. Operational experience has shown that most UAV disasters are due to human errors [2]. Furthermore, it takes several hours of flight and significant financial investment to train a human pilot who can handle these phases efficiently and safely.…”

“…Hardware-In-the-Loop (HIL) simulation and field experiments are conducted to demonstrate the performance of the entire test system and the proposed approach. An intelligent landing system based on an Artificial Neural Network (ANN) is given in [2]. The weights are searched using the Multi-dimensional Archive of Phenotypic Elites (MAP-Elites) algorithm.…”