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 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 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 chaos-based particle swarm optimization-ant colony optimization algorithm with asynchronous pheromone updating strategy for path planning of landfill inspection robots

2019 ◽  
Vol 16 (4) ◽  
pp. 172988141985908 ◽  
Author(s):  
Peng Chen ◽  
Qing Li ◽  
Chao Zhang ◽  
Jiarui Cui ◽  
Hao Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Ant Colony Optimization ◽  
Particle Swarm ◽  
Ant Colony ◽  
Planning Problem ◽  
Swarm Optimization ◽  
Gas Poisoning ◽  
Inspection Robots ◽  
Elitist Strategy

Robots are coming to help us in different harsh environments such as deep sea or coal mine. Waste landfill is the place like these with casualty risk, gas poisoning, and explosion hazards. It is reasonable to use robots to fulfill tasks like burying operation, transportation, and inspection. In these assignments, one important issue is to obtain appropriate paths for robots especially in some complex applications. In this context, a novel hybrid swarm intelligence algorithm, ant colony optimization enhanced by chaos-based particle swarm optimization, is proposed in this article to deal with the path planning problem for landfill inspection robots in Asahikawa, Japan. In chaos-based particle swarm optimization, Chebyshev chaotic sequence is used to generate the random factors for particle swarm optimization updating formula so as to effectively adjust particle swarm optimization parameters. This improved model is applied to optimize and determine the hyper parameters for ant colony optimization. In addition, an improved pheromone updating strategy which combines the global asynchronous feature and “Elitist Strategy” is employed in ant colony optimization in order to use global information more appropriately. Therefore, the iteration number of ant colony optimization invoked by chaos-based particle swarm optimization can be reduced reasonably so as to decrease the search time effectively. Comparative simulation experiments show that the chaos-based particle swarm optimization-ant colony optimization has a rapid search speed and can obtain solutions with similar qualities.

Application of Particle Swarm Optimization to Robot Path Planning Problem

2010 ◽  
Author(s):  
Masakazu Kobayashi ◽  
Higashi Masatake
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Particle Swarm ◽  
Degree Of Freedom ◽  
Planning Problem ◽  
Robot Path Planning ◽  
Swarm Optimization ◽  
Articulated Robot ◽  
Path Planning Problem ◽  
Robot Path

A robot path planning problem is to produce a path that connects a start configuration and a goal configuration while avoiding collision with obstacles. To obtain a path for robots with high degree of freedom of motion such as an articulated robot efficiently, sampling-based algorithms such as probabilistic roadmap (PRM) and rapidly-exploring random tree (RRT) were proposed. In this paper, a new robot path planning method based on Particle Swarm Optimization (PSO), which is one of heuristic optimization methods, is proposed in order to improve efficiency of path planning for a wider range of problems. In the proposed method, a group of particles fly through a configuration space while avoiding collision with obstacles and a collection of their trajectories is regarded as a roadmap. A velocity of each particle is updated for every time step based on the update equation of PSO. After explaining the details of the proposed method, this paper shows the comparisons of efficiency between the proposed method and RRT for 2D maze problems and then shows application of the proposed method to path planning for a 6 degree of freedom articulated robot.

scholarly journals Improved Particle Swarm Optimization of Three-Dimensional Path Planning for Fixed Wing Unmanned Aerial Vehicle

2019 ◽  
Vol 35 (2) ◽  
Author(s):  
Giang Thi - Huong Dang ◽  
Quang - Huy Vuong ◽  
Minh Hoang Ha ◽  
Minh - Trien Pham
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Particle Swarm ◽  
Adaptive Mutation ◽  
Planning Problem ◽  
Swarm Optimization ◽  
3D Environment ◽  
Aerial Vehicle ◽  
Improved Pso

Path planning for Unmanned Aerial Vehicle (UAV) targets at generating an optimal global path to the target, avoiding collisions and optimizing the given cost function under constraints. In this paper, the path planning problem for UAV in pre-known 3D environment is presented. Particle Swarm Optimization (PSO) was proved the efficiency for various problems. PSO has high convergence speed yet with its major drawback of premature convergence when solving large-scale optimization problems. In this paper, the improved PSO with adaptive mutation to overcome its drawback in order to applied PSO the UAV path planning in real 3D environment which composed of mountains and constraints. The effectiveness of the proposed PSO algorithm is compared to Genetic Algorithm, standard PSO and other improved PSO using 3D map of Daklak, Dakrong and Langco Beach. The results have shown the potential for applying proposed algorithm in optimizing the 3D UAV path planning. Keywords: UAV, Path planning, PSO, Optimization.

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.

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