Three-Dimensional Off-Line Path Planning for Unmanned Aerial Vehicle Using Modified Particle Swarm Optimization
This paper addresses the problem of offline path
planning for Unmanned Aerial Vehicles (UAVs) in complex threedimensional
environment with obstacles, which is modelled by 3D
Cartesian grid system. Path planning for UAVs require the
computational intelligence methods to move aerial vehicles along the
flight path effectively to target while avoiding obstacles. In this paper
Modified Particle Swarm Optimization (MPSO) algorithm is applied
to generate the optimal collision free 3D flight path for UAV. The
simulations results clearly demonstrate effectiveness of the proposed
algorithm in guiding UAV to the final destination by providing
optimal feasible path quickly and effectively.
[1] B. Taati, M. Greenspan, K. Gupta, “A dynamic load-balancing parallel
search for enumerative robot path planning,” Journal of Intelligent and
Robotic Systems, pp. 47: 55-85, 2006.
[2] K. Sugihara, J. Smith, “A Genetic Algorithm for 3-D Path Planning of a
Mobile Robot,.” white paper, Department of Information and Computer
Science, University of Hawaii, Manoa, Honolulu, 1999.
[3] F. Fahimi, “Autonomous Robots: Modeling, Path Planning, and
Control,” Canada: Mechanical Engineering Department University of
Alberta, 2008.
[4] A. Tsourdos, B. White, M. Shanmugavel, “Cooperative Path Planning of
Unmanned Aerial Vehicles,” John Wiley & Sons, West Sussex, UK,
2011.
[5] K.P. Valavanis, “Advances in Unmanned Aerial Vehicles,” State of the
Art the Road to Autonomy. Springer, 2007.
[6] S. Mittal, K. Deb, “Three-dimensional offline path planning for UAVs
using multiobjective evolutionary algorithms,” Proceedings of the IEEE
Congress on Evolutionary Computation, vol. 7, pp. 3195–3202, 2007.
[7] Y. Nikolos, N. Tsourveloudis, K. P. Valavanis, “Evolutionary Algorithm
Based off-line Path Planner for UAV Navigation,” Automatika, vol. 42,
no. 3-4, pp. 143-150, 2001.
[8] L. D. Jalal, “Modified Particle Swarm Optimization and Modified
Genetic Algorithm Approaches for Mobile Robot Navigation - A
Comparative Study,” University of Sulaimani, MSc thesis, 2013.
[9] A. Lazinica, “Particle swarm optimization,” In-Tech, Vienna, Austria,
2009.
[1] B. Taati, M. Greenspan, K. Gupta, “A dynamic load-balancing parallel
search for enumerative robot path planning,” Journal of Intelligent and
Robotic Systems, pp. 47: 55-85, 2006.
[2] K. Sugihara, J. Smith, “A Genetic Algorithm for 3-D Path Planning of a
Mobile Robot,.” white paper, Department of Information and Computer
Science, University of Hawaii, Manoa, Honolulu, 1999.
[3] F. Fahimi, “Autonomous Robots: Modeling, Path Planning, and
Control,” Canada: Mechanical Engineering Department University of
Alberta, 2008.
[4] A. Tsourdos, B. White, M. Shanmugavel, “Cooperative Path Planning of
Unmanned Aerial Vehicles,” John Wiley & Sons, West Sussex, UK,
2011.
[5] K.P. Valavanis, “Advances in Unmanned Aerial Vehicles,” State of the
Art the Road to Autonomy. Springer, 2007.
[6] S. Mittal, K. Deb, “Three-dimensional offline path planning for UAVs
using multiobjective evolutionary algorithms,” Proceedings of the IEEE
Congress on Evolutionary Computation, vol. 7, pp. 3195–3202, 2007.
[7] Y. Nikolos, N. Tsourveloudis, K. P. Valavanis, “Evolutionary Algorithm
Based off-line Path Planner for UAV Navigation,” Automatika, vol. 42,
no. 3-4, pp. 143-150, 2001.
[8] L. D. Jalal, “Modified Particle Swarm Optimization and Modified
Genetic Algorithm Approaches for Mobile Robot Navigation - A
Comparative Study,” University of Sulaimani, MSc thesis, 2013.
[9] A. Lazinica, “Particle swarm optimization,” In-Tech, Vienna, Austria,
2009.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:71375", author = "Lana Dalawr Jalal", title = "Three-Dimensional Off-Line Path Planning for Unmanned Aerial Vehicle Using Modified Particle Swarm Optimization", abstract = "This paper addresses the problem of offline path
planning for Unmanned Aerial Vehicles (UAVs) in complex threedimensional
environment with obstacles, which is modelled by 3D
Cartesian grid system. Path planning for UAVs require the
computational intelligence methods to move aerial vehicles along the
flight path effectively to target while avoiding obstacles. In this paper
Modified Particle Swarm Optimization (MPSO) algorithm is applied
to generate the optimal collision free 3D flight path for UAV. The
simulations results clearly demonstrate effectiveness of the proposed
algorithm in guiding UAV to the final destination by providing
optimal feasible path quickly and effectively.", keywords = "Obstacle Avoidance, Particle Swarm Optimization,
Three-Dimensional Path Planning Unmanned Aerial Vehicles.", volume = "9", number = "8", pages = "1579-5", }