Abstract
This paper proposed an improved particle swarm optimization (PSO) algorithm to solve the three-dimensional problem of path planning for the fixed-wing unmanned aerial vehicle (UAV) in the complex environment. The improved PSO algorithm (called DCA ∗ PSO) based dynamic divide-and-conquer (DC) strategy and modified A ∗ algorithm is designed to reach higher precision for the optimal flight path. In the proposed method, the entire path is divided into multiple segments, and these segments are evolved in parallel by using DC strategy, which can convert the complex high-dimensional problem into several parallel low-dimensional problems. In addition, A ∗ algorithm is adopted to generated an optimal path from the particle swarm, which can avoid premature convergence and enhance global search ability. When DCA ∗ PSO is used to solve the large-scale path planning problem, an adaptive dynamic strategy of the segment selection is further developed to complete an effective variable grouping according to the cost. To verify the optimization performance of DCA ∗ PSO algorithm, the real terrain data is utilized to test the performance for the route planning. The experiment results show that the proposed DCA ∗ PSO algorithm can effectively obtain better optimization results in solving the path planning problem of UAV, and it takes on better optimization ability and stability. In addition, DCA ∗ PSO algorithm is proved to search a feasible route in the complex environment with a large number of the waypoints by the experiment.
Highlights
In order to demonstrate the performances of the DCA∗particle swarm optimization (PSO) algorithm for the path planning problem, a series of experiments with the realistic environment maps [37] were implemented on a PC with Intel Core (TM) i7-9700 CPU
Because there were three turns in all 30 turns experiments cannot find a feasible solution for the DCA∗PSO algorithm, the standard deviation is increased by the penalty constant
DCA∗PSO is presented by introducing the improved A ∗ algorithm and DC strategy
Summary
[14], Huang [28] proposed an improved PSO algorithm based on the competition of global best solution and applied the path planner to a three-dimensional UAV to demonstrate. This paper studies the PSO-based approach with dynamic divide-andconquer (DC) strategy and modified A ∗ algorithm for solving the path planning problem. To deal with the challenges of the traditional PSO algorithm, a dynamic DC strategy and modified A ∗ algorithm are proposed for the path planning problem of UAV. On the basis of analyses of the cost function and constraints for UAVs, a new path planner is formulated to enhance the ability of solving high-dimensional route planning problem in complex 3D environment.
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