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CLC number: TP241

On-line Access: 2015-07-06

Received: 2014-11-04

Revision Accepted: 2015-06-16

Crosschecked: 2015-06-17

Cited: 2

Clicked: 1819

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Zi-wu Ren

http://orcid.org/0000-0002-3774-2273

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Frontiers of Information Technology & Electronic Engineering  2015 Vol.16 No.7 P.607-616

http://doi.org/10.1631/FITEE.14a0335


A hybrid biogeography-based optimization method for the inverse kinematics problem of an 8-DOF redundant humanoid manipulator


Author(s):  Zi-wu Ren, Zhen-hua Wang, Li-ning Sun

Affiliation(s):  School of Computer Science & Technology, Soochow University, Suzhou 215021, China; more

Corresponding email(s):   zwren@suda.edu.cn, wangzhenhua@suda.edu.cn, wzh@hit.edu.cn

Key Words:  Inverse kinematics problem, 8-DOF humanoid manipulator, Biogeography-based optimization (BBO), Differential evolution (DE)


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Zi-wu Ren, Zhen-hua Wang, Li-ning Sun. A hybrid biogeography-based optimization method for the inverse kinematics problem of an 8-DOF redundant humanoid manipulator[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(7): 607-616.

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Abstract: 
The redundant humanoid manipulator has characteristics of multiple degrees of freedom and complex joint structure, and it is not easy to obtain its inverse kinematics solution. The inverse kinematics problem of a humanoid manipulator can be formulated as an equivalent minimization problem, and thus it can be solved using some numerical optimization methods. biogeography-based optimization (BBO) is a new biogeography inspired optimization algorithm, and it can be adopted to solve the inverse kinematics problem of a humanoid manipulator. The standard BBO algorithm that uses traditional migration and mutation operators suffers from slow convergence and prematurity. A hybrid biogeography-based optimization (HBBO) algorithm, which is based on BBO and differential evolution (DE), is presented. In this hybrid algorithm, new habitats in the ecosystem are produced through a hybrid migration operator, that is, the BBO migration strategy and DE/best/1/bin differential strategy, to alleviate slow convergence at the later evolution stage of the algorithm. In addition, a Gaussian mutation operator is adopted to enhance the exploration ability and improve the diversity of the population. Based on these, an 8-DOF (degree of freedom) redundant humanoid manipulator is employed as an example. The end-effector error (position and orientation) and the ‘away limitation level’ value of the 8-DOF humanoid manipulator constitute the fitness function of HBBO. The proposed HBBO algorithm has been used to solve the inverse kinematics problem of the 8-DOF redundant humanoid manipulator. Numerical simulation results demonstrate the effectiveness of this method.

This paper proposes a hybrid optimization algorithm of Biogeography-based Optimization (HBBO) based on the improvement of BBO algorithm and differential evolution (DE) to deal with the inverse kinematics problem of humanoid manipulator. The algorithm allows for generation of new habitats in ecosystem through a hybrid migration operator, which can speed up the convergence of the algorithm. Gaussian mutation operator is also used to enhance the exploration ability and improve the diversity of the population. Numerical simulation has been performed to demonstrate the effectiveness of the proposed method to determine the solution for the inverse kinematics of an 8-DOF redundant humanoid manipulator, which also shows its potentials to be applied to practice in real time, and other redundant manipulators as well. The paper presents the novelty for solution of the inverse kinematics.

基于混合生物地理学优化的8自由度冗余臂逆运动学求解

目的:针对多自由度且关节结构复杂并带有关节位置物理约束的冗余仿人臂系统,研究其逆运动学问题的求解。
创新点:提出一种BBO和DE算法相融合的混合生物地理学优化方法(HBBO),并将其应用于8自由度冗余臂逆运动学问题求解中,并取得了良好的求解效果。
方法:冗余臂逆运动学问题可以转化为等效的最小化问题,并可采用数值方法求解。首先,提出一种BBO和DE算法相融合的混合生物地理学优化方法(算法3)。该方法使用混合迁移策略,即标准BBO迁移与DE/best/1/bin差分策略,生成新栖息地(算法1),并采用高斯变异操作改善群体的多样性(算法2)。然后,以冗余仿人臂末端位姿误差和“远离限位度”指标构建优化目标函数,采用混合生物地理学优化方法求解8自由度冗余臂逆运动学问题。与SGA、DE及BBO方法比较,本文方法求解该问题所获得的结果更优(图2、表3),仿人臂连杆构型也验证了其末端位姿满足期望要求(图4)。
结论:提出了基于混合生物地理学优化(HBBO)的8自由度冗余仿人臂逆运动学问题数值求解方法。与常规方法比较,该方法求解精度更高。

关键词:逆运动学;8自由度冗余仿人臂;生物地理学优化;差分进化

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