UAV Path Planning under Dynamic Threats Using an Improved PSO Algorithm

Abstract

This paper presents the method to solve the problem of path planning for an unmanned aerial vehicle (UAV) in adversarial environments including radar-guided surface-to-air missiles (SAMs) and unknown threats. SAM lethal envelope and radar detection for SAM threats and line-of-sight (LOS) calculation for unknown threats are considered to compute the cost for path planning. In particular, dynamic SAM lethal envelope is taken into account for path planning in that SAM lethal envelope does change its direction according to the flight direction of UAV. In addition, terrain masking, nonisotropic radar cross section (RCS), and dynamic constraints of UAV are considered to determine the cost of the path. An improved particle swarm optimization (PSO) algorithm is proposed for finding an optimal path. The proposed algorithm is composed of preprocessing steps, multi-swarm PSO algorithm, and postprocessing steps. The Voronoi diagram and Dijkstra algorithm as preprocessing steps provide the initial path for the multi-swarm PSO algorithm which uses multiple swarms with sub-swarms for the balance between exploration and exploitation. Postprocessing steps include waypoint insertion and 3D path smoothing. The computation time is reduced by using the map generation, the coordinate transformation, and the graphic processing unit (GPU) implementation of the algorithm. Various simulations are carried out to compare the performance of the proposed method according to the number of iterations, the number of swarms, and the number of cost evaluation points. The t -test results show that the suggested method is statistically better than existing methods.

Path Planning of Unmanned Helicopter in Complex Environment Based on Heuristic Deep Q-Network

“…The local path planning algorithm can make corresponding decisions according to the local environment information and explore the passable path when the global information is unknown by interacting with the environment. The more representative algorithms include genetic algorithm, dynamic window approach, ant colony algorithm, particle swarm algorithm, and artificial potential field method [15][16][17][18][19][20]. In the literature [15], the authors find the optimal flight path for the UAV by using an improved genetic algorithm with a new genetic factor on the basis of the probability map.…”

Path Planning for Unmanned Aerial Vehicle Using a Mix-Strategy-Based Gravitational Search Algorithm

“…Recently, the population-based meta-heuristic algorithms that can handle constrained optimization problems have become the efficient and effective techniques for path planning [2,[12][13][14][15][16][17][18][19][20]. By designing specific objective functions or constraint functions, both the self-constraints of UAV and environmental constraints can be taken into account during the planning process [21][22][23].…”

“…In addition, the artificial bee colony (ABC) [13], ant colony optimization (ACO) [14], water drops optimization (WDO) [15], and their variations have also been utilized to search the optimal flight path for single or multiple UAVs. More critical situation such as scenarios with disaster and dynamic threats usually desires improved intelligent algorithms [18][19][20]. More recently, the development of deep learning also spawned the deep reinforcement learning based path planning [24].…”

Prediction of China’s Express Business Volume Based on FGM (1, 1) Model

“…Particle Swarm Optimization (PSO) algorithm has been proposed in 1995 by Kennedy and Eberhart [14,15] based on the behavior of birds foraging, which is widely used in various calculations [16][17][18]. According to the data from China's express business from 2015-2019, this paper uses the improved particle swarm optimization algorithm to solve the fractional-order r of the FGM (1, 1) model and then predicts the express business volume of China in the coming years through the FGM (1, 1) model.…”