The preliminary study on multi-swarm sharing particle swarm optimization: Applied to UAV path planning problem

2011 ◽  
Author(s):  
Chih-Li Huo ◽  
Tzu-Ying Lai ◽  
Tsung-Ying Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Particle Swarm ◽  
Planning Problem ◽  
Swarm Optimization ◽  
Preliminary Study ◽  
Path Planning Problem

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 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 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.

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.

Robotic Path Planning Using Flower Pollination Algorithm

2020 ◽  
Vol 13 (2) ◽  
pp. 191-199
Author(s):  
Ishita Mehta ◽  
Geetika Singh ◽  
Yogita Gigras ◽  
Anuradha Dhull ◽  
Priyanka Rastogi
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Flower Pollination Algorithm ◽  
Planning Problem ◽  
Swarm Optimization ◽  
Flower Pollination ◽  
Cpu Time ◽  
Path Planning Problem

Background: Robotic path planning is an important facet of robotics. Its purpose is to make robots move independently in their work environment from a source to a destination whilst satisfying certain constraints. Constraint conditions are as follows: avoiding collision with obstacles, staying as far as possible from the obstacles, traversing the shortest path, taking minimum time, consuming minimum energy and so on. Hence, the robotic path planning problem is a conditional constraint optimization problem. Methods: To overcome this problem, the Flower Pollination Algorithm, which is a metaheuristic approach is employed. The effectiveness of Flower Pollination Algorithm is showcased by using diverse maps. These maps are composed of several fixed obstacles in different positions, a source and a target position. Initially, the pollinators carrying pollen (candidate solutions) are at the source location. Subsequently, the pollinators must pave a way towards the target location while simultaneously averting any obstacles that are encountered enroute. The pollinators should also do so with the minimum cost possible in terms of distance. The performance of the algorithm in terms of CPU time is evaluated. Flower Pollination Algorithm was also compared to the Particle Swarm Optimization algorithm and Ant Colony Optimization algorithm. Results: It was observed that Flower Pollination Algorithm is faster than Particle Swarm Optimization and Ant Colony Optimization in terms of CPU time for the same number of iterations to find an optimized solution for robotic path planning. Conclusion: The Flower Pollination Algorithm can be effectively applied for solving robotic path planning problem with static obstacles.

scholarly journals Constrained Path Planning for Unmanned Aerial Vehicle in 3D Terrain Using Modified Multi-Objective Particle Swarm Optimization

Actuators ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 255
Author(s):  
Shuang Xia ◽  
Xiangyin Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Gaussian Distribution ◽  
Planning Problem ◽  
Swarm Optimization ◽  
Q Learning ◽  
Multi Objective ◽  
Aerial Vehicle ◽  
Path Planning Problem

This paper considered the constrained unmanned aerial vehicle (UAV) path planning problem as the multi-objective optimization problem, in which both costs and constraints are treated as the objective functions. A novel multi-objective particle swarm optimization algorithm based on the Gaussian distribution and the Q-Learning technique (GMOPSO-QL) is proposed and applied to determine the feasible and optimal path for UAV. In GMOPSO-QL, the Gaussian distribution based updating operator is adopted to generate new particles, and the exploration and exploitation modes are introduced to enhance population diversity and convergence speed, respectively. Moreover, the Q-Learning based mode selection logic is introduced to balance the global search with the local search in the evolution process. Simulation results indicate that our proposed GMOPSO-QL can deal with the constrained UAV path planning problem and is superior to existing optimization algorithms in terms of efficiency and robustness.

scholarly journals A Routing-Based Repair Method for Digital Microfluidic Biochips Based on an Improved Dijkstra and Improved Particle Swarm Optimization Algorithm

Micromachines ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1052
Author(s):  
Wenbin Zheng ◽  
Jinlong Shi ◽  
Anqi Wang ◽  
Ping Fu ◽  
Hongyuan Jiang
Keyword(s):  
Particle Swarm Optimization ◽  
Completion Time ◽  
Design Method ◽  
Particle Swarm ◽  
Planning Problem ◽  
Swarm Optimization ◽  
Repair Method ◽  
Digital Microfluidic ◽  
Path Planning Problem ◽  
The Greedy Algorithm

Digital microfluidic biochips (DMFBs) are attractive instruments for obtaining modern molecular biology and chemical measurements. Due to the increasingly complex measurements carried out on a DMFB, such chips are more prone to failure. To compensate for the shortcomings of the module-based DMFB, this paper proposes a routing-based fault repair method. The routing-based synthesis methodology ensures a much higher chip utilization factor by removing the virtual modules on the chip, as well as removing the extra electrodes needed as guard cells. In this paper, the routing problem is identified as a dynamic path-planning problem and mixed path design problem under certain constraints, and an improved Dijkstra and improved particle swarm optimization (ID-IPSO) algorithm is proposed. By introducing a cost function into the Dijkstra algorithm, the path-planning problem under dynamic obstacles is solved, and the problem of mixed path design is solved by redefining the position and velocity vectors of the particle swarm optimization. The ID-IPSO routing-based fault repair method is applied to a multibody fluid detection experiment. The proposed design method has a stronger optimization ability than the greedy algorithm. The algorithm is applied to 8×9, 8×8, and 7×8 fault-free chips. The proposed ID-IPSO routing-based chip design method saves 13.9%, 14.3%, and 14.5% of the experiment completion time compared with the greedy algorithm. Compared with a modular fault repair method based on the genetic algorithm, the ID-IPSO routing-based fault repair method has greater advantages and can save 39.3% of the completion time on average in the completion of complex experiments. When the ratio of faulty electrodes is less than 12% and 23%, the modular and ID-IPSO routing-based fault repair methods, respectively, can guarantee a 100% failure repair rate. The utilization rate of the electrodes is 18% higher than that of the modular method, and the average electrode usage time is 17%. Therefore, the ID-IPSO routing-based fault repair method can accommodate more faulty electrodes for the same fault repair rate; the experiment completion time is shorter, the average number of electrodes is lower, and the security performance is better.

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 Particle Swarm Optimization of Non Uniform Rational B-Splines for Robot Manipulators Path Planning

2017 ◽  
Vol 61 (4) ◽  
pp. 337
Author(s):  
Nadjib Zerrouki ◽  
Noureddine Goléa ◽  
Nabil Benoudjit
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Robot Manipulator ◽  
Particle Swarm ◽  
Planning Problem ◽  
Swarm Optimization ◽  
New Approach ◽  
B Splines ◽  
Evolutionary Technique ◽  
Additional Constraints

The path-planning problem is commonly formulated to handle the obstacle avoidance constraints. This problem becomes more complicated when further restrictions are added. It often requires efficient algorithms to be solved. In this paper, a new approach is proposed where the path is described by means of Non Uniform Rational B-Splines (NURBS for short) with more additional constraints. An evolutionary technique called Particle Swarm Optimization (PSO) with three options of particles velocity updating offering three alternatives namely the PSO with inertia weight (PSO-W), the constriction factor PSO (PSO-C) and the combination of the two(PSO-WC); are used to optimize the weights of the control points that serve as parameters of the algorithm describing the path. Simulation results show how the mixture of the first two options produces a powerful algorithm, specifically (PSO-WC), in producing a compromise between fast convergence and large number of potential solution. In addition, the whole approach seems to be flexible, powerful and useful for the generation of successful smooth trajectories for robot manipulator which are independent from environment conditions.

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