Path planning of robot based on adaptive genetic algorithm considering multiple factors
LI Hang-yu, GUO Xiao-li
Path planning of robot based on adaptive genetic algorithm considering multiple factors
[1] |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
[2] |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
[3] |
This paper proposes the optimization of a fully connected recurrent neural network (FCRNN) using advanced multiobjective continuous ant colony optimization (AMO-CACO) for the multiobjective gait generation of a biped robot (the NAO). The FCRNN functions as a central pattern generator and is optimized to generate angles of the hip roll and pitch, the knee pitch, and the ankle pitch and roll. The performance of the FCRNN-generated gait is evaluated according to the walking speed, trajectory straightness, oscillations of the body in the pitch and yaw directions, and walking posture, subject to the basic constraints that the robot cannot fall down and must walk forward. This paper formulates this gait generation task as a constrained multiobjective optimization problem and solves this problem through an AMO-CACO-based evolutionary learning approach. The AMO-CACO finds Pareto optimal solutions through ant-path selection and sampling operations by introducing an accumulated rank for the solutions in each single-objective function into solution sorting to improve learning performance. Simulations are conducted to verify the AMO-CACO-based FCRNN gait generation performance through comparisons with different multiobjective optimization algorithms. Selected software-designed Pareto optimal FCRNNs are then applied to control the gait of a real NAO robot.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
[4] |
Seyyed Mohammad Hosseini Rostami, Arun Kumar Sangaiah,
{{custom_citation.content}}
{{custom_citation.annotation}}
|
[5] |
孟冠军, 陈信华, 陶细佩, 等. 基于混合蚁群算法的AGV路径规划[J]. 组合机床与自动化加工技术, 2021,(01):70-73.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
[6] |
张林, 詹威鹏, 朱玲芬, 等. 基于优化人工蜂群算法的多机器人协同规划[J]. 机械传动, 2017, 41(12):129-132,145.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
[7] |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
[8] |
魏彤, 龙琛. 基于改进遗传算法的移动机器人路径规划[J]. 北京航空航天大学学报, 2020, 46(04):703-711.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
[9] |
徐梦颖, 王娇娇, 刘宝, 等. 基于改进遗传算法的机器人路径规划[J/OL]. 石河子大学学报(自然科学版):1-6[2021-01-29]. https://doi.org/10.13880/j.cnki.65-1174/n.2020.21.046.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
[10] |
孙波, 姜平, 周根荣, 等. 基于改进遗传算法的AGV路径规划[J]. 计算机工程与设计, 2020, 41(02):550-556.
{{custom_citation.content}}
{{custom_citation.annotation}}
|
{{custom_ref.label}} |
{{custom_citation.content}}
{{custom_citation.annotation}}
|
/
〈 | 〉 |