scholarly journals A Path Planning Method Based on Adaptive Genetic Algorithm for a Shape-shifting Robot

2010 ◽  
Vol 3 (4) ◽  
Author(s):  
Mengxin Li ◽  
Xinghua Xia ◽  
Ying Zhang ◽  
Tonglin Liu
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Planning Method ◽  
Adaptive Genetic Algorithm

Hurry Path Planning Based on Adaptive Genetic Algorithm

2013 ◽  
Vol 446-447 ◽  
pp. 1292-1297 ◽  
Author(s):  
Da Qiao Zhang ◽  
Jiu Fen Zhao ◽  
Gang Lei ◽  
Shun Hong Wang ◽  
Xiao Long Zheng
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Formation Flying ◽  
Planning Method ◽  
High Mode ◽  
Adaptive Genetic Algorithm ◽  
Aerial Vehicles ◽  
Simulation Results ◽  
Risk Cost

During formation flying, Unmanned Aerial Vehicles (UAV) may need to arrive at target ahead of schedule by hurry path. Given fixed flight high mode, hurry planning method was proposed based on Adaptive Genetic Algorithm (AGA), which makes the new path shorter by locally adjusting the default path. By full considering the risk of UAV flight, the hurry path got by AGA meets the requirements of the risk cost and time amount in advance. Simulation results show that the path gotten by AGA can better meet the requirements of the time amount in advance, and evade the threat area effectively too.

A New Path Planning Method of Robot Based on Improved Adaptive Genetic Algorithms

2013 ◽  
Vol 278-280 ◽  
pp. 590-593
Author(s):  
Huai Qiang Li ◽  
Ming Xia Shi
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Path Planning ◽  
Simulation Experiment ◽  
Planning Method ◽  
Adaptive Genetic Algorithm ◽  
Robot Path Planning ◽  
Adaptive Genetic Algorithms ◽  
Planning Space ◽  
Robot Path

This paper presents a new method of robot path planning which is based on improved adaptive genetic algorithm. On the foundation of building the model in planning space by link-graph, we first gets the feasible paths by using Ford algorithms ,and then adjusts the points of every path by using improved adaptive genetic algorithms to get the best or better path. The simulation experiment shows the advancement of the method.

scholarly journals Autonomous navigation in unknown environment using sliding mode SLAM and genetic algorithm

2021 ◽  
Author(s):  
Salvador Ortiz ◽  
Wen Yu
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Sliding Mode Control ◽  
Autonomous Navigation ◽  
Sliding Mode ◽  
Simultaneous Localization And Mapping ◽  
Planning Method ◽  
Unknown Environment ◽  
Localization And Mapping ◽  
Bounded Uncertainties

In this paper, sliding mode control is combined with the classical simultaneous localization and mapping (SLAM) method. This combination can overcome the problem of bounded uncertainties in SLAM. With the help of genetic algorithm, our novel path planning method shows many advantages compared with other popular methods.

scholarly journals Research on the Food Logistic Optimal Path Planning Method of Improving the Genetic Algorithm

2016 ◽  
Vol 11 (4) ◽  
pp. 269-273
Author(s):  
Li Si ◽  
Wang Yuan ◽  
Li Xinzhong ◽  
Liu Shenyang ◽  
Li Zhen
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Optimal Path ◽  
Planning Method ◽  
Optimal Path Planning

Vehicle Path Planning Method Based on Particle Swarm Optimization Algorithm

2012 ◽  
Vol 468-471 ◽  
pp. 2745-2748
Author(s):  
Sheng Long Yu ◽  
Yu Ming Bo ◽  
Zhi Min Chen ◽  
Kai Zhu
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Path Planning ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Adaptive Genetic Algorithm ◽  
Swarm Optimization ◽  
Vehicle Path

A particle swarm optimization algorithm (PSO) is presented for vehicle path planning in the paper. Particle swarm optimization proposed by Kennedy and Eberhart is derived from the social behavior of the birds foraging. Particle swarm optimization algorithm a kind of swarm-based optimization method.The simulation experiments performed in this study show the better vehicle path planning ability of PSO than that of adaptive genetic algorithm and genetic algorithm. The experimental results show that the vehicle path planning by using PSO algorithm has the least cost and it is indicated that PSO algorithm has more excellent vehicle path planning ability than adaptive genetic algorithm,genetic algorithm.

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