A Robotic Path Planning by Using Crow Swarm Optimization Algorithm
Mohammed Yousif, Ahmad Salim, Wisam K. Jummar
- 发表年份
- 2021
- 引用次数
- 8
- 访问权限
- 开放获取
摘要
One of the most common problem in the design of robotic technology is the path planning. The challenge is choosing the robotics' path from source to destination with minimum cost. Meta-heuristic algorithms are popular tools used in a search process to get optimal solution. In this paper, we used Crow Swarm Optimization (CSO) to overcome the problem of choosing the optimal path without collision. The results of CSO compared with two meta-heuristic algorithms: PSO and ACO in addition to a hybrid method between these algorithms. The comparison process illustrates that the CSO better than PSO and ACO in path planning, but compared to hybrid method CSO was better whenever the smallest population. Consequently, the importance of research lies in finding a new method to use a new metahumanistic algorithm to solve the problem of robotic path planning.
关键词
相关论文
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002