scholarly journals An Adaptive Parallel Arithmetic Optimization Algorithm for Robot Path Planning

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Ruo-Bin Wang ◽  
Wei-Feng Wang ◽  
Lin Xu ◽  
Jeng-Shyang Pan ◽  
Shu-Chuan Chu
Keyword(s):  
Path Planning ◽  
Optimization Algorithm ◽  
Optimal Solution ◽  
Communication Strategy ◽  
Motion Path ◽  
Robot Path Planning ◽  
Parallel Arithmetic ◽  
Local Optimal Solution ◽  
Solution Accuracy ◽  
Robot Path

Path planning is one of the hotspots in the research of automotive engineering. Aiming at the issue of robot path planning with the goal of finding a collision-free optimal motion path in an environment with barriers, this study proposes an adaptive parallel arithmetic optimization algorithm (APAOA) with a novel parallel communication strategy. Comparisons with other popular algorithms on 18 benchmark functions are committed. Experimental results show that the proposed algorithm performs better in terms of solution accuracy and convergence speed, and the proposed strategy can prevent the algorithm from falling into a local optimal solution. Finally, we apply APAOA to solve the problem of robot path planning.

A new path planning method of mobile robot based on adaptive dynamic firefly algorithm

2020 ◽  
Vol 34 (29) ◽  
pp. 2050322
Author(s):  
Guanghui Xu ◽  
Ting-Wei Zhang ◽  
Qiang Lai ◽  
Jian Pan ◽  
Bo Fu ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
High Performance ◽  
Firefly Algorithm ◽  
Optimal Solution ◽  
Robot Path Planning ◽  
Adaptive Parameters ◽  
Local Optimal Solution ◽  
Reaction Speed ◽  
Robot Path

Path planning has always been a hot topic in the field of mobile robot research. At present, the mainstream issues of the mobile robot path planning are combined with the swarm intelligence algorithms. Among them, the firefly algorithm is more typical. The firefly algorithm has the advantages of simple concepts and easy implementation, but it also has the disadvantages of being easily trapped into a local optimal solution, with slow convergence speed and low accuracy. To better combine the path planning of mobile robot with firefly algorithm, this paper studies the optimization firefly algorithm for the path planning of mobile robot. By using the strategies of optimizing the adaptive parameters in the firefly algorithm, an adaptive firefly algorithm is designed to solve the problem that the firefly algorithm is easy to get into the local optimal solution and improves the performance of firefly algorithm. The optimized algorithm with high performance can improve the computing ability and reaction speed of the mobile robot in the path planning. Finally, the theoretical and experimental results have verified the effectiveness and superiority of the proposed algorithm, which can meet the requirements of the mobile robot path planning.

The Robot Path Planning Based on Region Partition to Node Optimization

2011 ◽  
Vol 383-390 ◽  
pp. 605-609
Author(s):  
Yun Shan Liu
Keyword(s):  
Path Planning ◽  
Dynamic Optimization ◽  
Optimal Solution ◽  
Robot Path Planning ◽  
Analog Simulation ◽  
New Approach ◽  
Region Partition ◽  
Local Optimal Solution ◽  
Low Efficiency ◽  
Robot Path

We put forward the concept that introducing the methods of region partition and node optimization into original optimization of AOC, in order to solve the problems that ACO’S low efficiency in original execution, huge computational complexity in the process of conclusion, mess route and easily trap into the local optimal solution. The number and location of urban node after dynamic optimization reduce the ant colony quantity and iterative time arithmetic. The optimization improves the execution efficiency of arithmetic, and at the same time the analog simulation successfully applies to the robot path planning design, which show that the method is efficient and applicable so as to create a new approach of improve ACO.

Robot Path Planning Based on Ant Colony Optimization Algorithm for Environments with Obstacles

2017 ◽  
pp. 175-184 ◽  
Author(s):  
Rudolf Neydorf ◽  
Orhan Yarakhmedov ◽  
Victor Polyakh ◽  
Ivan Chernogorov ◽  
Dean Vucinic
Keyword(s):  
Path Planning ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony ◽  
Robot Path Planning ◽  
Ant Colony Optimization Algorithm ◽  
Robot Path

Mobile Robot Path Planning by Improved Brain Storm Optimization Algorithm

2018 ◽  
Author(s):  
Eva Tuba ◽  
Ivana Strumberger ◽  
Dejan Zivkovic ◽  
Nebojsa Bacanin ◽  
Milan Tuba
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimization Algorithm ◽  
Robot Path Planning ◽  
Brain Storm Optimization ◽  
Robot Path

scholarly journals An Improved Chicken Swarm Optimization Algorithm and its Application in Robot Path Planning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 49543-49550 ◽  
Author(s):  
Ximing Liang ◽  
Dechang Kou ◽  
Long Wen
Keyword(s):  
Path Planning ◽  
Optimization Algorithm ◽  
Robot Path Planning ◽  
Swarm Optimization ◽  
Chicken Swarm Optimization ◽  
Robot Path

Path Planning for Mobile Robot with Clonal Selection Algorithm

2012 ◽  
Vol 256-259 ◽  
pp. 2943-2946
Author(s):  
Yi Li ◽  
Zhen Hui Song ◽  
Li Zhao
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Clonal Selection ◽  
Optimal Solution ◽  
Clonal Selection Algorithm ◽  
Mutation Operator ◽  
Robot Path Planning ◽  
Selection Algorithm ◽  
Evolutionary Progress ◽  
Robot Path

Aiming at the problem of path planning for a mobile robot, an oriented clonal selection algorithm is proposed. Firstly, the static environment was expressed by a map with nodes and links. Secondly, the locations of target and obstacles were defined. Thirdly, an oriented mutation operator was used to accelerate the evolutionary progress. In this way, we can find an optimal solution with proposed oriented clonal algorithm. Experiment results demonstrate that the algorithm is simple, effective, to solve the problem of robot path planning in a static environment

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