Energy Efficient UAV Flight Control Method in an Environment with Obstacles and Gusts of Wind

Abstract

This article presents an energy-efficient method of controlling unmanned aircraft (fixed-wing UAVs), which consists of three groups of algorithms: aerial vehicle route planning, in-flight control, and algorithms to correct the preplanned flight trajectory. All algorithms shall take into account the existence of obstacles that the UAV must avoid and wind gusts in the UAV’s area of operation. Tests were carried out on the basis of the UAV mathematical model, stabilization and navigation algorithms, and Dryden turbulence model, considering the parameters of the UAV’s propulsion system. The work includes a detailed description of constructing a network of connection that is used to plan a UAV mission. It presents the algorithm for determining the actual distances between the different points in the field of action, which takes into account the existence of obstacles. The algorithm shall be based on methods for determining the flight trajectory on a hexagonal grid. It presents the developed proprietary UAV path planning algorithm based on a model from a group of algorithms of mixed integer linear problem (MILP) optimization. It presents the manner in which the pre-prepared flight path was used by UAV controllers that supervised the flight along the preset path. It details the architecture of contemporary unmanned aerial vehicles, which have embedded capability to realize autonomous missions, which require the integration of UAV systems into the route planning algorithms set out in the article. Particular attention has been paid to the planning and implementation methods of UAV missions under conditions where wind gusts are present, which support the determination of UAV flight routes to minimize the vehicle’s energy consumption. The models developed were tested within a computer architecture based on ARM processors using the hardware-in-the-loop (HIL) technique, which is commonly used to control unmanned vehicles. The presented solution makes use of two computers: FCC (flight control computer) based on a real-time operating system (RTOS) and MC (mission computer) based on Linux and integrated with the Robot Operating System (ROS). A new contribution of this work is the integration of planning and monitoring methods for the implementation of missions aimed at minimizing energy consumption of the vehicle, taking into account wind conditions.

Quadrotor Trajectory Control Based on Energy-Optimal Reference Generator

“…This model was specifically developed to analyze the energy efficiency of the quadrotor during different maneuvers. In the domain of fixed-wing UAVs, [26] explored an energy-efficient control method based on determining flight trajectories using a hexagonal grid. This investigation contributed to the ongoing efforts to enhance the energy efficiency and autonomy of unmanned aircraft systems through advanced control strategies.…”

“…. By comparing (26) and ( 27) to ( 28) and (29), and by utilizing the small angle condition cos θ ≈ 1 in the denominator of (26), explicit approximate expressions for the reference angles (ϕ, θ) are derived. These expressions effectively impose the desired dynamics in (28) and (29).…”

A Comprehensive Review of Recent Research Trends on Unmanned Aerial Vehicles (UAVs)

“…Achieving energy efficiency is essential to enhance the performance and capabilities of UAVs, including flight time, payload capacity, and range. Researchers are exploring several approaches [103], such as aerodynamic design optimization [103], lightweight materials, and the integration of alternative energy sources such as solar power [104]. Reducing the weight of UAVs is one of the key challenges in achieving energy-efficient flight.…”

Gust Modeling with State-of-the-Art Computational Fluid Dynamics (CFD) Software and Its Influence on the Aerodynamic Characteristics of an Unmanned Aerial Vehicle

“…Thus, studies on UAVs flight control methods in a gusty environment seem to be of great importance. Such research was conducted by Chodnicki M. et al [10].…”