Abstract
Three-dimension path planning for uninhabited combat air vehicle (UCAV) is a complicated high-dimension optimization problem, which primarily centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. A new hybrid metaheuristic differential evolution (DE) and cuckoo search (CS) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize the process of selecting cuckoos of the improved CS model during the process of cuckoo updating in nest. The cuckoos can act as an agent in searching the optimal UCAV path. And then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic CS. The realization procedure for this hybrid metaheuristic approach DE/CS is also presented. In order to make the optimized UCAV path more feasible, the B-Spline curve is adopted for smoothing the path. To prove the performance of this proposed hybrid metaheuristic method, it is compared with basic CS algorithm. The experiment shows that the proposed approach is more effective and feasible in UCAV three-dimension path planning than the basic CS model.
Highlights
Unmanned combat aerial vehicles (UAVs) are remotely piloted or self-piloted aircrafts that can carry many different types of accessories such as cameras, sensors, and communications equipment
In order to make the optimized uninhabited combat air vehicle (UCAV) path more feasible, the B-Spline curve is adopted for smoothing the path
This paper presented a hybrid metaheuristic cuckoo search (CS) and differential evolution (DE) algorithm for UCAV three-dimension path planning in complicated combat field environments
Summary
Unmanned combat aerial vehicles (UAVs) are remotely piloted or self-piloted aircrafts that can carry many different types of accessories such as cameras, sensors, and communications equipment. In 1995, Storn and Price firstly proposed a novel evolutionary algorithm (EA): differential evolution (DE) [9, 10], which is a new heuristic approach for minimizing possibly nonlinear and nondifferentiable continuous space functions It converges faster and with more certainty than many other acclaimed global population-based optimization methods [11]. To verify the feasibility and effectiveness of our proposed approach, the series experiments conducted under complicated combating environment demonstrate that our hybrid metaheuristic approach with B-Spline curve path smoothing can generate a feasible optimal three-dimension path of UCAV more quickly than the basic CS algorithm.
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