Trajectory Generation and Optimization Using the Mutual Learning and Adaptive Ant Colony Algorithm in Uneven Environments

Trajectory Generation and Optimization Using the Mutual Learning and Adaptive Ant Colony Algorithm in Uneven Environments

Aiming at the trajectory generation and optimization of mobile robots in complex and uneven environments, a hybrid scheme using mutual learning and adaptive ant colony optimization (MuL-ACO) is proposed in this paper. In order to describe the uneven environment with various obstacles, a 2D-H map is...

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Bibliographic Details
Main Authors: Feng Qu, Wentao Yu, Kui Xiao, Chaofan Liu, Weirong Liu
Format: Article
Language:English
Published: MDPI AG 2022-05-01
Series:Applied Sciences
Subjects:
trajectory generation and optimization
mutual learning
adaptive ant colony algorithm
2D-H map
de-temperature function
Online Access:https://www.mdpi.com/2076-3417/12/9/4629

Internet

https://www.mdpi.com/2076-3417/12/9/4629

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