Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Advanced Algorithms for Autonomous Guidance of Solar-powered UAVs

Siyuan Li

TL;DR

This paper addresses the endurance limitations of UAVs by focusing on solar-powered UAVs (SUAVs) and their energy-aware navigation in urban environments. It surveys global, local, and hybrid path-planning approaches and integrates energy harvesting models, including cloud-integrated and cloud-free solar harvesting, with line-of-sight shading considerations. It contributes multiple targeted navigation strategies tailored to SUAVs: a privacy-aware dynamic-programming path, energy-aware 3D urban planners using modified A*-like methods, a time-energy trade-off planner, and a three-phase hybrid approach for dynamic environments, validated through simulations. The results demonstrate that incorporating solar energy models and energy-aware planning yields longer endurance, safer operation, and efficient mission completion, while outlining future directions such as RIS-enabled communications, uneven terrain navigation, and multi-UAV coordination with learning-based energy management.

Abstract

Unmanned aerial vehicle (UAV) techniques have developed rapidly within the past few decades. Using UAVs provides benefits in numerous applications such as site surveying, communication systems, parcel delivery, target tracking, etc. The high manoeuvrability of the drone and its ability to replace a certain amount of labour cost are the reasons why it can be widely chosen. There will be more applications of UAVs if they can have longer flight time, which is a very challenging hurdle because of the energy constraint of the onboard battery. One promising solution is to equip UAVs with some lightweight solar panels to maximize flight time. Therefore, more research is needed for solar-powered UAVs (SUAVs) in different environments.

Advanced Algorithms for Autonomous Guidance of Solar-powered UAVs

TL;DR

This paper addresses the endurance limitations of UAVs by focusing on solar-powered UAVs (SUAVs) and their energy-aware navigation in urban environments. It surveys global, local, and hybrid path-planning approaches and integrates energy harvesting models, including cloud-integrated and cloud-free solar harvesting, with line-of-sight shading considerations. It contributes multiple targeted navigation strategies tailored to SUAVs: a privacy-aware dynamic-programming path, energy-aware 3D urban planners using modified A*-like methods, a time-energy trade-off planner, and a three-phase hybrid approach for dynamic environments, validated through simulations. The results demonstrate that incorporating solar energy models and energy-aware planning yields longer endurance, safer operation, and efficient mission completion, while outlining future directions such as RIS-enabled communications, uneven terrain navigation, and multi-UAV coordination with learning-based energy management.

Abstract

Unmanned aerial vehicle (UAV) techniques have developed rapidly within the past few decades. Using UAVs provides benefits in numerous applications such as site surveying, communication systems, parcel delivery, target tracking, etc. The high manoeuvrability of the drone and its ability to replace a certain amount of labour cost are the reasons why it can be widely chosen. There will be more applications of UAVs if they can have longer flight time, which is a very challenging hurdle because of the energy constraint of the onboard battery. One promising solution is to equip UAVs with some lightweight solar panels to maximize flight time. Therefore, more research is needed for solar-powered UAVs (SUAVs) in different environments.
Paper Structure (76 sections, 49 equations, 19 figures, 5 tables, 3 algorithms)

This paper contains 76 sections, 49 equations, 19 figures, 5 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Background
  3. Navigation Methods
  4. Global Path Planning
  5. Search-based Methods
  6. Sample-based Methods
  7. Other Mentionable Methods
  8. Local Path Planning
  9. Hybrid Path Planning
  10. Report Organization
  11. Literature Review
  12. Applications of SUAVs
  13. Vision Based Applications
  14. Target Surveillance
  15. Environmental Applications
  16. ...and 61 more sections

Figures (19)

  • Figure 1: 2D: UAV trajectories for PA, OAA, A*A and RRTA
  • Figure 2: 2D: Cost Function for PA, OAA, A*A and RRTA
  • Figure 4: 3D: Top View
  • Figure 6: Illustration of Urban Environment
  • Figure 7: Illustration of Shadow Area
  • ...and 14 more figures