Three-Dimensional Path Planning of Unmanned Aerial Vehicles Using Particle Swarm Optimization

2006 ◽  
Author(s):  
Jung Leng Foo ◽  
Jared Knutzon ◽  
James Oliver ◽  
Eliot Winer
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Three Dimensional ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Aerial Vehicles

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.

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 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 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 Pareto Optimal PID Tuning for Px4-Based Unmanned Aerial Vehicles by Using a Multi-Objective Particle Swarm Optimization Algorithm

Aerospace ◽  
2020 ◽  
Vol 7 (6) ◽  
pp. 71
Author(s):  
Victor Gomez ◽  
Nicolas Gomez ◽  
Jorge Rodas ◽  
Enrique Paiva ◽  
Maarouf Saad ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Unmanned Aerial Vehicles ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Pid Tuning ◽  
Aerial Vehicles ◽  
The Stability ◽  
Selection Of

Unmanned aerial vehicles (UAVs) are affordable these days. For that reason, there are currently examples of the use of UAVs in recreational, professional and research applications. Most of the commercial UAVs use Px4 for their operating system. Even though Px4 allows one to change the flight controller structure, the proportional-integral-derivative (PID) format is still by far the most popular choice. A selection of the PID controller parameters is required before the UAV can be used. Although there are guidelines for the design of PID parameters, they do not guarantee the stability of the UAV, which in many cases, leads to collisions involving the UAV during the calibration process. In this paper, an offline tuning procedure based on the multi-objective particle swarm optimization (MOPSO) algorithm for the attitude and altitude control of a Px4-based UAV is proposed. A Pareto dominance concept is used for the MOPSO to find values for the PID comparing parameters of step responses (overshoot, rise time and root-mean-square). Experimental results are provided to validate the proposed tuning procedure by using a quadrotor as a case study.

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