Performance Evaluation of Three Intelligent Optimization Algorithms for Obstacle Avoidance Path Planning

Author(s):  
Shumeng Chen ◽  
Chia-Hung Wang ◽  
Zhenyu Dong ◽  
Qigen Zhao ◽  
Qipeng Yang ◽  
...  
2019 ◽  
pp. 609-648 ◽  
Author(s):  
Xiujuan Lei ◽  
Fei Wang ◽  
Ying Tan

Mobile robot path planning is generally a kind of optimal problems, which is to find a best path of a track between a starting point to a goal point in the constraint conditions. Mobile robot path planning can be divided into two categories according to different environment planning awareness information: one is the global path planning and the other is the local path planning. We employed ACO, PSO, FA, FOA, FWA and ABC swarm intelligent optimization algorithms to optimize the global and local path planning of mobile robot, and gave the detailed implement steps and the comparing results to show the feasibility of using swarm intelligence optimization algorithms to solve the robot path planning problems.


2021 ◽  
Vol 1 (1) ◽  
pp. 15-32
Author(s):  
Wenyin Gong ◽  
Zuowen Liao ◽  
Xianyan Mi ◽  
Ling Wang ◽  
Yuanyuan Guo

Author(s):  
Jihong Liu ◽  
Sen Zeng

Assembly planning is one of the NP complete problems, which is even more difficult to solve for complex products. Intelligent optimization algorithms have obvious advantages to deal with such combinatorial problems. Various intelligent optimization algorithms have been applied to assembly sequence planning and optimization in the last decade. This paper surveys the state-of-the-art of the assembly planning methods based on the intelligent optimization algorithms. Five intelligent optimization algorithms, i.e. genetic algorithm (GA), artificial neural networks (ANN), simulated annealing (SA), ant colony algorithm (ACO) and artificial immune algorithm (AIA), and their applications in assembly planning and optimization are introduced respectively. The application features of the algorithms are summarized. At last, the future research directions of the assembly planning based on the intelligent optimization algorithms are discussed.


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