scholarly journals Distributed 3-D Path Planning for Multi-UAVs with Full Area Surveillance Based on Particle Swarm Optimization

2021 ◽  
Vol 11 (8) ◽  
pp. 3417
Author(s):  
Nafis Ahmed ◽  
Chaitali J. Pawase ◽  
KyungHi Chang
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Fitness Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Human Beings ◽  
Swarm Optimization ◽  
Aerial Vehicles ◽  
Area Surveillance

Collision-free distributed path planning for the swarm of unmanned aerial vehicles (UAVs) in a stochastic and dynamic environment is an emerging and challenging subject for research in the field of a communication system. Monitoring the methods and approaches for multi-UAVs with full area surveillance is needed in both military and civilian applications, in order to protect human beings and infrastructure, as well as their social security. To perform the path planning for multiple unmanned aerial vehicles, we propose a trajectory planner based on Particle Swarm Optimization (PSO) algorithm to derive a distributed full coverage optimal path planning, and a trajectory planner is developed using a dynamic fitness function. In this paper, to obtain dynamic fitness, we implemented the PSO algorithm independently in each UAV, by maximizing the fitness function and minimizing the cost function. Simulation results show that the proposed distributed path planning algorithm generates feasible optimal trajectories and update maps for the swarm of UAVs to surveil the entire area of interest.

scholarly journals Enhanced Particle Swarm Optimization for Path Planning of Unmanned Aerial Vehicles

2020 ◽  
Vol 14 (1) ◽  
pp. 67-78
Author(s):  
Kai Yit Kok ◽  
Parvathy Rajendran
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Aerial Vehicles ◽  
Trial And Error Method ◽  
The Impact ◽  
Enhanced Particle Swarm Optimization ◽  
Error Method

This paper presents an enhanced particle swarm optimization (PSO) for the path planning of unmanned aerial vehicles (UAVs). An evolutionary algorithm such as PSO is costly because every application requires different parameter settings to maximize the performance of the analyzed parameters. People generally use the trial-and-error method or refer to the recommended setting from general problems. The former is time consuming, while the latter is usually not the optimum setting for various specific applications. Hence, this study focuses on analyzing the impact of input parameters on the PSO performance in UAV path planning using various complex terrain maps with adequate repetitions to solve the tuning issue. Results show that inertial weight parameter is insignificant, and a 1.4 acceleration coefficient is optimum for UAV path planning. In addition, the population size between 40 and 60 seems to be the optimum setting based on the case studies.

Path Planning of Unmanned Aerial Vehicles using B-Splines and Particle Swarm Optimization

2009 ◽  
Vol 6 (4) ◽  
pp. 271-290 ◽  
Author(s):  
Jung Leng Foo ◽  
Jared Knutzon ◽  
Vijay Kalivarapu ◽  
James Oliver ◽  
Eliot Winer
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
B Splines ◽  
Aerial Vehicles

scholarly journals Hybrid Form of Particle Swarm Optimization and Genetic Algorithm For Optimal Path Planning in Coverage Mission by Cooperated Unmanned Aerial Vehicles

2020 ◽  
Author(s):  
Hassan Haghighi ◽  
Seyed Hossein Sadati ◽  
S.M. Mehdi Dehghan ◽  
Jalal Karimi
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Particle Swarm ◽  
Computational Time ◽  
Planning Problem ◽  
Hybrid Form ◽  
Swarm Optimization ◽  
Aerial Vehicles

In this paper, a new form of open traveling salesman problem (OTSP) is used for path planning for optimal coverage of a wide area by cooperated unmanned aerial vehicles (UAVs). A hybrid form of particle swarm optimization (PSO) and genetic algorithm (GA) is developed for the current path planning problem of multiple UAVs in the coverage mission. Three path-planning approaches are introduced through a group of the waypoints in a mission area: PSO, genetic algorithm, and a hybrid form of parallel PSO-genetic algorithm. The proposed hybrid optimization tries to integrate the advantages of the PSO, i.e. coming out from local minimal, and genetic algorithm, i.e. better quality solutions within a reasonable computational time. These three approached are compared in many scenarios with different levels of difficulty. Statistical analyses reveal that the hybrid algorithm is a more effective strategy than others for the mentioned problem.

scholarly journals Optimal Design of Multimissile Formation Based on an Adaptive SA-PSO Algorithm

Aerospace ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 21
Author(s):  
Shuangxi Liu ◽  
Fengping Huang ◽  
Binbin Yan ◽  
Tong Zhang ◽  
Ruifan Liu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Index System ◽  
Fitness Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Evaluation Index System ◽  
Design Parameters ◽  
Swarm Optimization ◽  
Adaptive Pso

In an effort to maximize the combat effectiveness of multimissile groups, this paper proposes an adaptive simulated annealing–particle swarm optimization (SA-PSO) algorithm to enhance the design parameters of multimissile formations based on the concept of missile cooperative engagement. Firstly, considering actual battlefield circumstances, we establish an effectiveness evaluation index system for the cooperative engagement of missile formations based on the analytic hierarchy process (AHP). In doing so, we adopt a partial triangular fuzzy number method based on authoritative assessments by experts to ascertain the weight of each index. Then, considering given constraints on missile performance, by selecting the relative distances and angles of the leader and follower missiles as formation parameters, we design a fitness function corresponding to the established index system. Finally, we introduce an adaptive capability into the traditional particle swarm optimization (PSO) algorithm and propose an adaptive SA-PSO algorithm based on the simulated annealing (SA) algorithm to calculate the optimal formation parameters. A simulation example is presented for the scenario of optimizing the formation parameters of three missiles, and comparative experiments conducted with the traditional and adaptive PSO algorithms are reported. The simulation results indicate that the proposed adaptive SA-PSO algorithm converges faster than both the traditional and adaptive PSO algorithms and can quickly and effectively solve the multimissile formation optimization problem while ensuring that the optimized formation satisfies the given performance constraints.

scholarly journals A Novel Three-Dimensional Path Planning Method for Fixed-Wing UAV Using Improved Particle Swarm Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Chen Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Three Dimensional ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Planning Problem ◽  
Dimensional Problem ◽  
Complex Environment ◽  
Swarm Optimization ◽  
Path Planning Problem

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.

scholarly journals A Novel Particle Swarm Optimization Algorithm Based on Reinforcement Learning Mechanism for AUV Path Planning

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Haoqian Huang ◽  
Chao Jin
Keyword(s):  
Particle Swarm Optimization ◽  
Reinforcement Learning ◽  
Path Planning ◽  
Autonomous Underwater Vehicle ◽  
Synthesis Method ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Feedback Mechanism ◽  
Swarm Optimization ◽  
Learning Mechanism

In order to solve the problems of rapid path planning and effective obstacle avoidance for autonomous underwater vehicle (AUV) in 2D underwater environment, this paper proposes a path planning algorithm based on reinforcement learning mechanism and particle swarm optimization (RMPSO). A feedback mechanism of reinforcement learning is embedded into the particle swarm optimization (PSO) algorithm by using the proposed RMPSO to improve the convergence speed and adaptive ability of the PSO. Then, the RMPSO integrates the velocity synthesis method with the Bezier curve to eliminate the influence of ocean currents and save energy for AUV. Finally, the path is developed rapidly and obstacles are avoided effectively by using the RMPSO. Simulation and experiment results show the superiority of the proposed method compared with traditional methods.

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