Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Energy-Optimized Planning in Non-Uniform Wind Fields with Fixed-Wing Aerial Vehicles

Yufei Duan, Florian Achermann, Jaeyoung Lim, Roland Siegwart

TL;DR

This work addresses energy-efficient path planning for fixed-wing sUAVs operating in non-uniform wind fields near complex terrain. It presents a sampling-based planner that uses ground-relative Dubins airplane paths (via an iteration-friendly approach) together with a physically motivated energy model, enabling the UAV to exploit updrafts while maintaining safety and real-time planning capability. The paper demonstrates, through synthetic and realistic wind-field experiments (including WRF-generated winds), that energy-optimal trajectories can substantially reduce energy use at the cost of longer flight times, and that ground-relative planning provides a robust onboard-friendly formulation. These results advance long-endurance missions by enabling energy-aware routing in realistic wind environments and offer practical guidance on when to prefer ground-relative versus air-relative planning under wind uncertainty.

Abstract

Fixed-wing small uncrewed aerial vehicles (sUAVs) possess the capability to remain airborne for extended durations and traverse vast distances. However, their operation is susceptible to wind conditions, particularly in regions of complex terrain where high wind speeds may push the aircraft beyond its operational limitations, potentially raising safety concerns. Moreover, wind impacts the energy required to follow a path, especially in locations where the wind direction and speed are not favorable. Incorporating wind information into mission planning is essential to ensure both safety and energy efficiency. In this paper, we propose a sampling-based planner using the kinematic Dubins aircraft paths with respect to the ground, to plan energy-efficient paths in non-uniform wind fields. We study the planner characteristics with synthetic and real-world wind data and compare its performance against baseline cost and path formulations. We demonstrate that the energy-optimized planner effectively utilizes updrafts to minimize energy consumption, albeit at the expense of increased travel time. The ground-relative path formulation facilitates the generation of safe trajectories onboard sUAVs within reasonable computational timeframes.

Energy-Optimized Planning in Non-Uniform Wind Fields with Fixed-Wing Aerial Vehicles

TL;DR

This work addresses energy-efficient path planning for fixed-wing sUAVs operating in non-uniform wind fields near complex terrain. It presents a sampling-based planner that uses ground-relative Dubins airplane paths (via an iteration-friendly approach) together with a physically motivated energy model, enabling the UAV to exploit updrafts while maintaining safety and real-time planning capability. The paper demonstrates, through synthetic and realistic wind-field experiments (including WRF-generated winds), that energy-optimal trajectories can substantially reduce energy use at the cost of longer flight times, and that ground-relative planning provides a robust onboard-friendly formulation. These results advance long-endurance missions by enabling energy-aware routing in realistic wind environments and offer practical guidance on when to prefer ground-relative versus air-relative planning under wind uncertainty.

Abstract

Fixed-wing small uncrewed aerial vehicles (sUAVs) possess the capability to remain airborne for extended durations and traverse vast distances. However, their operation is susceptible to wind conditions, particularly in regions of complex terrain where high wind speeds may push the aircraft beyond its operational limitations, potentially raising safety concerns. Moreover, wind impacts the energy required to follow a path, especially in locations where the wind direction and speed are not favorable. Incorporating wind information into mission planning is essential to ensure both safety and energy efficiency. In this paper, we propose a sampling-based planner using the kinematic Dubins aircraft paths with respect to the ground, to plan energy-efficient paths in non-uniform wind fields. We study the planner characteristics with synthetic and real-world wind data and compare its performance against baseline cost and path formulations. We demonstrate that the energy-optimized planner effectively utilizes updrafts to minimize energy consumption, albeit at the expense of increased travel time. The ground-relative path formulation facilitates the generation of safe trajectories onboard sUAVs within reasonable computational timeframes.
Paper Structure (24 sections, 13 equations, 5 figures, 2 tables)

This paper contains 24 sections, 13 equations, 5 figures, 2 tables.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. PROBLEM FORMULATION
  4. Dubins Airplane Model
  5. Air Relative Velocity
  6. APPROACH
  7. Ground-Relative Dubins Airplane Model in Wind
  8. Fixed-Wing Energy Model
  9. Feasibility
  10. ENERGY-OPTIMIZED PLANNING
  11. Planner
  12. Cost Objective
  13. Feasibility
  14. EVALUATION SETUP
  15. Setup
  16. ...and 9 more sections

Figures (5)

  • Figure 1: Planned paths around mountainous terrain with different cost formulations colored by the encountered vertical wind ($W_z$). The energy-optimized path (E-opt) leverages the updrafts on the left side of the mountain leading to a path with lower energy required but longer flight time compared to the time-optimized path (T-opt). The minimum-distance path (D-opt) disregards any wind information and is infeasible to track due to the strong downdrafts encountered along the path.
  • Figure 2: State space of the Dubins airplane model with the air relative (superscript $\mathcal{A}$) and ground-relative (superscript $\mathcal{G}$) properties.
  • Figure 3: Energy-optimized paths in a) Horizontal Shear environment, b) Updraft environment, explain the different paths d for shortest path, e for energy, t for time, subscript for wind field magnitude
  • Figure 4: The performance metrics of the different cost objectives and path formulations for the realistic wind fields. The shortest path planning (dg) is compared to time-optimized planning with air-relative paths (ta) and ground-relative (tg) and energy-optimized planning with the two path types (ea and eg).
  • Figure 5: The planned paths for case 2 with WRF generated winds colored by the encountered vertical wind. The energy-optimized path (E-opt) leverages the updrafts and avoids strong downdrafts resulting in a longer path compared to the time-optimized path (T-opt) that goes through a strong downdraft. The shortest path (D-opt) does not consider any wind information and is infeasible to track due to the strong downdraft around the middle.