Quantum-entanglement pigeon-inspired optimization for unmanned aerial vehicle path planning

2018 ◽  
Vol 91 (1) ◽  
pp. 171-181 ◽  
Author(s):  
Siqi Li ◽  
Yimin Deng
Keyword(s):  
Path Planning ◽  
Quantum Entanglement ◽  
Unmanned Aerial Vehicle ◽  
Quantum Effect ◽  
Market Place ◽  
Content Type ◽  
Aerial Vehicle ◽  
Stable Performance ◽  
Vehicle Path ◽  
Planning Problems

Purpose The purpose of this paper is to propose a new algorithm for independent navigation of unmanned aerial vehicle path planning with fast and stable performance, which is based on pigeon-inspired optimization (PIO) and quantum entanglement (QE) theory. Design/methodology/approach A biomimetic swarm intelligent optimization of PIO is inspired by the natural behavior of homing pigeons. In this paper, the model of QEPIO is devised according to the merging optimization of basic PIO algorithm and dynamics of QE in a two-qubit XXZ Heisenberg System. Findings Comparative experimental results with genetic algorithm, particle swarm optimization and traditional PIO algorithm are given to show the convergence velocity and robustness of our proposed QEPIO algorithm. Practical implications The QEPIO algorithm hold broad adoption prospects because of no reliance on INS, both on military affairs and market place. Originality/value This research is adopted to solve path planning problems with a new aspect of quantum effect applied in parameters designing for the model with the respective of unmanned aerial vehicle path planning.

scholarly journals A Cauchy mutant pigeon-inspired optimization–based multi-unmanned aerial vehicle path planning method

2020 ◽  
Vol 53 (1-2) ◽  
pp. 83-92 ◽  
Author(s):  
Bo Hang Wang ◽  
Dao Bo Wang ◽  
Zain Anwar Ali
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Optimization Algorithm ◽  
Vehicle Group ◽  
Flight Duration ◽  
Planning Strategy ◽  
Optimum Position ◽  
Cauchy Mutation ◽  
Aerial Vehicle ◽  
Vehicle Path

To improve the performance of multi-unmanned aerial vehicle path planning in plateau narrow area, a control strategy based on Cauchy mutant pigeon-inspired optimization algorithm is proposed in this article. The Cauchy mutation operator is chosen to improve the pigeon-inspired optimization algorithm by comparing and analyzing the changing trend of fitness function of the local optimum position and the global optimum position when dealing with unmanned aerial vehicle path planning problems. The plateau topography model and plateau wind field model are established. Furthermore, a variety of control constrains of unmanned aerial vehicles are summarized and modeled. By combining with relative positions and total flight duration, a cooperative path planning strategy for unmanned aerial vehicle group is put forward. Finally, the simulation results show that the proposed Cauchy mutant pigeon-inspired optimization method gives better robustness and cooperative path planning strategy which are effective and advanced as compared with traditional pigeon-inspired optimization algorithm.

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