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Chaotic artificial bee colony approach to step planning of maintaining balance for quadruped robot

机译:四足机器人保持平衡的混沌人工蜂群方法

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Purpose - Artificial bee colony (ABC) algorithm is a relatively new optimization method inspired by the herd behavior of honey bees, which shows quite intelligence. The purpose of this paper is to propose an improved ABC optimization algorithm based on chaos theory for solving the push recovery problem of a quadruped robot, which can tune the controller parameters based on its search mechanism. ADAMS simulation environment is adopted to implement the proposed scheme for the quadruped robot. Design/methodology/approach - Maintaining balance is a rather complicated global optimum problem for a quadruped robot which is about seeking a foot contact point prevents itself from falling down. To ensure the stability of the intelligent robot control system, the intelligent optimization method is employed. The proposed chaotic artificial bee colony (CABC) algorithm is based on basic ABC, and a chaotic mechanism is used to help the algorithm to jump out of the local optimum as well as finding the optimal parameters. The implementation procedure of our proposed chaotic ABC approach is described in detail. Findings - The proposed CABC method is applied to a quadruped robot in ADAMS simulator. Using the CABC to implement, the quadruped robot can work smoothly under the interference. A comparison among the basic ABC and CABC is made. Experimental results verify a better trajectory tracking response can be achieved by the proposed CABC method after control parameters training. Practical implications - The proposed CABC algorithm can be easily applied to practice and can steer the robot during walking, which will considerably increase the autonomy of the robot. Originality/value - The proposed CABC approach is interesting for the optimization of a control scheme for quadruped robot. A parameter training methodology, using the presented intelligent algorithm is proposed to increase the learning capability. The experimental results verify the system stabilization, favorable performance and no chattering phenomena can be achieved by using the proposed CABC algorithm. And, the proposed CABC methodology can be easily extended to other applications.
机译:目的-人工蜂群(ABC)算法是一种相对较新的优化方法,其灵感来自蜜蜂的从众行为,它具有相当的智能性。本文的目的是提出一种改进的基于混沌理论的ABC优化算法,以解决四足机器人的推挽恢复问题,该算法可以基于其搜索机制来调整控制器参数。采用ADAMS仿真环境来实现四足机器人的建议方案。设计/方法/方法-对于四足机器人,保持平衡是一个相当复杂的全局最优问题,该问题是寻找脚部接触点以防止自身跌落。为了保证智能机器人控制系统的稳定性,采用了智能优化方法。提出的混沌人工蜂群算法(CABC)是基于基本的ABC算法,采用混沌机制帮助算法跳出局部最优解并找到最优参数。详细介绍了我们提出的混沌ABC方法的实现过程。结果-拟议的CABC方法应用于ADAMS模拟器中的四足机器人。使用CABC来实现,四足机器人可以在干扰下平稳运行。在基本ABC和CABC之间进行了比较。实验结果证明,在控制参数训练后,提出的CABC方法可以实现较好的轨迹跟踪响应。实际意义-所提出的CABC算法可以轻松地应用于实践,并且可以在步行过程中操纵机器人,这将大大增加机器人的自主性。独创性/价值-提出的CABC方法对于优化四足机器人的控制方案很有趣。提出了一种使用本文提出的智能算法进行参数训练的方法,以提高学习能力。实验结果验证了所提出的CABC算法的稳定性,良好的性能以及无抖动现象。并且,所提出的CABC方法可以容易地扩展到其他应用。

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