Path Planning of Mobile Robot Based on Simulated Annealing Particle Swarm Optimization Algorithm

2020 ◽  
pp. 131-139
Author(s):  
Jie Zhao ◽  
Xuesong Sheng ◽  
Jianghao Shi
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Path Planning ◽  
Mobile Robot ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization

scholarly journals A Heuristic Elastic Particle Swarm Optimization Algorithm for Robot Path Planning

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 99 ◽  
Author(s):  
Haiyan Wang ◽  
Zhiyu Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Mobile Robots ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimal Path ◽  
Particle Swarm ◽  
A Algorithm ◽  
Swarm Optimization ◽  
Elastic Particle

Path planning, as the core of navigation control for mobile robots, has become the focus of research in the field of mobile robots. Various path planning algorithms have been recently proposed. In this paper, in view of the advantages and disadvantages of different path planning algorithms, a heuristic elastic particle swarm algorithm is proposed. Using the path planned by the A* algorithm in a large-scale grid for global guidance, the elastic particle swarm optimization algorithm uses a shrinking operation to determine the globally optimal path formed by locally optimal nodes so that the particles can converge to it rapidly. Furthermore, in the iterative process, the diversity of the particles is ensured by a rebound operation. Computer simulation and real experimental results show that the proposed algorithm not only overcomes the shortcomings of the A* algorithm, which cannot yield the shortest path, but also avoids the problem of failure to converge to the globally optimal path, owing to a lack of heuristic information. Additionally, the proposed algorithm maintains the simplicity and high efficiency of both the algorithms.

Task Allocation and Path Planning of a Multi-Robot System Using Heuristic Coupled Particle Swarm Optimization Algorithm

2020 ◽  
pp. 194-209
Author(s):  
Arindam Majumder ◽  
Rajib Ghosh
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Optimization Algorithm ◽  
Task Allocation ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
A Algorithm ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization ◽  
Multi Robot

This study deals with a plant layout where there were ninety predefined locations which have to be inspected by using three multiple robots in such a way that there would not be any collisions between the robots. A heuristic integrated multiobjective particle swarm optimization algorithm (HPSO) is developed for allocating tasks to each robot and planning of path while moving from one task location to another. For optimal path planning of each robot the research utilized A* algorithm. The task allocation for each robot is carried out using a modified multiobjective particle swarm optimization algorithm where the earliest completion time (ECT) inspired technique is used to make the algorithm applicable in multi robot task allocation problems. At the later stage of this study, in order to test the capability of HPSO an instance is solved by the algorithm and is compared with the existing solutions of a genetic algorithm with the A* algorithm. The computational results showed the superiority of the proposed algorithm over existing algorithms.

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