Wind-energy based path planning for unmanned aerial vehicles using Markov decision processes
Al-Sabban, Wesam H., Gonzalez, Luis F., & Smith, Ryan N. (2013) Wind-energy based path planning for unmanned aerial vehicles using Markov decision processes. In Proceedings - IEEE International Conference on Robotics and Automation, IEEE, Kongresszentrum Karlsruhe, Karlsruhe, Germany, pp. 784-789.
Administrators only | Request a copy from author
Exploiting wind-energy is one possible way to extend flight duration for Unmanned Arial Vehicles. Wind-energy can also be used to minimise energy consumption for a planned path. In this paper, we consider uncertain time-varying wind fields and plan a path through them. A Gaussian distribution is used to determine uncertainty in the Time-varying wind fields. We use Markov Decision Process to plan a path based upon the uncertainty of Gaussian distribution. Simulation results that compare the direct line of flight between start and target point and our planned path for energy consumption and time of travel are presented. The result is a robust path using the most visited cell while sampling the Gaussian distribution of the wind field in each cell.
Impact and interest:
Citation counts are sourced monthly from and citation databases.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
|Item Type:||Conference Paper|
|Keywords:||Markov Decision Processes, wind-based planning, unmanned aerial vehicle, Gaussian Processes, energy optimization|
|Subjects:||Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > APPLIED MATHEMATICS (010200) > Dynamical Systems in Applications (010204)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Adaptive Agents and Intelligent Robotics (080101)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > AEROSPACE ENGINEERING (090100) > Flight Dynamics (090106)
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > Institutes > Institute for Future Environments
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2012 The Authors|
|Deposited On:||23 Sep 2012 22:31|
|Last Modified:||30 Apr 2014 18:49|
Repository Staff Only: item control page