本文選題：雙足機器人 + 平衡控制　； 參考：《電子科技大學(xué)》2017年碩士論文

【摘要】：驅(qū)動技術(shù),人工智能,高性能計算機等最新技術(shù)已經(jīng)使雙足機器人有了粗略模擬人體運動的靈巧性,能夠進行舞蹈展示,樂器演奏,與人交談等。然而這與投入實際應(yīng)用所需求的能力還有不小差距。主要體現(xiàn)在缺乏與人類相近的平衡能力和步伐協(xié)調(diào)能力,對工作環(huán)境要求高,在非結(jié)構(gòu)化環(huán)境中適應(yīng)能力差。因此,本文以自主研制的雙足機器人為研究對象,重點研究了雙足機器人的平衡控制,阻抗控制以及步態(tài)規(guī)劃等內(nèi)容。本文首先簡要介紹了自主研制的雙足機器人的軟硬件構(gòu)架,建立了ADAMS和Gazebo仿真來協(xié)助對控制算法性能預(yù)測和優(yōu)化并減少對物理機器人的危險操作。接著分析了雙足機器人的正逆運動學(xué)并引入運動學(xué)庫KDL來簡化運動學(xué)運算。穩(wěn)定的平衡控制對于雙足機器人而言在目前還是個不小的挑戰(zhàn)。本文就此研究了兩種處理平衡的阻抗調(diào)節(jié)方案。一種是基于LQR的固定阻抗模型,這種方案簡單有效,但存在易產(chǎn)生振動的問題,本文結(jié)合濾波改善了平衡控制效果。另一種是基于增強學(xué)習(xí)的自適應(yīng)阻抗模型。該方法可以在不知道系統(tǒng)內(nèi)部動態(tài)信息的情況下利用迭代策略在線得到最優(yōu)解,是對前述LQR方法的進一步優(yōu)化。隨后本文通過仿真和實驗進行了驗證并分析了優(yōu)缺點。步態(tài)規(guī)劃是機器人運動控制中最基礎(chǔ)的一環(huán)。本文從五連桿平面機器人入手對其運動控制進行了研究。首先采用基于ZMP的多項式擬合法實現(xiàn)了機器人平地行走的步態(tài)規(guī)劃。然后分析其動力學(xué)模型并利用PD控制器進行運動仿真,就仿真中出現(xiàn)雙腿支撐階段跟蹤誤差較大的問題提出了PD與徑向基神經(jīng)網(wǎng)絡(luò)混合控制的新策略。再次通過仿真證實該方案能夠減小跟蹤誤差。最后,本文利用前述多項式擬合法對實驗平臺的物理機器人進行靜態(tài)行走和上樓梯的步態(tài)規(guī)劃。針對上樓梯的步態(tài)規(guī)劃的特殊性,本文提出了分段擬合來實現(xiàn)各關(guān)節(jié)的協(xié)同規(guī)劃,并引入了軀干前傾角來輔助身體平衡。由于時間所限,本文實現(xiàn)了雙足機器人的穩(wěn)定步行實驗,上樓梯實驗還尚缺穩(wěn)健性,這將作為下一步的工作。
[Abstract]:The latest technologies, such as driving technology, artificial intelligence and high performance computer, have enabled biped robots to have the dexterity of simulating human motion roughly, to perform dance displays, to play musical instruments, to talk to people, and so on. However, there is still a big gap between the capacity required for practical applications. It is mainly reflected in the lack of balance ability and step coordination ability which is similar to that of human beings, the high requirement of work environment and the poor adaptability in unstructured environment. Therefore, this paper focuses on the balance control, impedance control and gait planning of the self-developed biped robot. In this paper, the software and hardware architecture of the self-developed biped robot is briefly introduced, and Adams and Gazebo simulations are established to help predict and optimize the performance of the control algorithm and reduce the dangerous operation of the physical robot. Then the forward and inverse kinematics of biped robot is analyzed and the kinematics library KDL is introduced to simplify kinematics operation. Stable balance control is still a big challenge for biped robots. In this paper, two kinds of impedance control schemes are studied. One is a fixed impedance model based on LQR, which is simple and effective, but easy to produce vibration. The other is an adaptive impedance model based on reinforcement learning. This method can obtain the optimal solution online without knowing the dynamic information of the system. It is a further optimization of the LQR method mentioned above. Then, the paper verifies and analyzes the advantages and disadvantages by simulation and experiment. Gait planning is the most basic link in robot motion control. In this paper, the motion control of five-bar planar robot is studied. Firstly, the plane-walking gait planning of the robot is realized by using the polynomial fitting method based on ZMP. Then the dynamic model is analyzed and the PD controller is used to simulate the motion. A new hybrid control strategy of PD and radial basis function neural network is proposed to solve the problem of large tracking error in the leg support stage. The simulation results show that the scheme can reduce the tracking error. Finally, this paper uses the polynomial fitting method to carry out the static walking and gait planning of the physical robot on the experimental platform. In view of the particularity of gait planning for stairs, a piecewise fitting method is proposed to realize the joint coordination planning, and a torso anteversion angle is introduced to assist the body balance. Due to the limitation of time, the steady walking experiment of biped robot is realized in this paper, and the stair experiment is still short of robustness, which will be the next step.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP242

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本文編號：2087947