【摘要】：近些年,在星際探測(cè)、復(fù)雜地形的搶險(xiǎn)救災(zāi)、海底作業(yè)、野外監(jiān)測(cè)、軍事偵查等特殊危險(xiǎn)作業(yè)行業(yè)中,任務(wù)難度越來(lái)越高,不適合人類去完成,這促使人們不斷研究機(jī)器人技術(shù),用機(jī)器人代替人類去完成艱巨的任務(wù)。本文針對(duì)帶有變形關(guān)節(jié)的新型六足仿生機(jī)器人,深入研究了其單腿運(yùn)動(dòng)學(xué)、動(dòng)力學(xué)和運(yùn)動(dòng)控制及并聯(lián)機(jī)身運(yùn)動(dòng)學(xué)。主要工作如下:(1)利用旋量理論與指數(shù)積方法建立腿部運(yùn)動(dòng)學(xué)模型并給出了運(yùn)動(dòng)學(xué)顯式逆解,針對(duì)腿部后兩關(guān)節(jié)軸線交于一點(diǎn)的特點(diǎn),結(jié)合經(jīng)典消元理論和Paden-Kahan子問(wèn)題方法計(jì)算其運(yùn)動(dòng)學(xué)顯式逆解;基于旋量法計(jì)算擺動(dòng)腿的雅可比矩陣,仿真結(jié)果驗(yàn)證了其正確性。(2)為了提高六足機(jī)器人腿部控制的平穩(wěn)性、動(dòng)作快速性,利用五次B樣條曲線進(jìn)行了關(guān)節(jié)空間軌跡規(guī)劃,從而使關(guān)節(jié)運(yùn)動(dòng)的速度、加速度和脈動(dòng)連續(xù)。利用B樣條曲線的凸包性把腿部關(guān)節(jié)速度與加速度的曲線約束轉(zhuǎn)化為對(duì)曲線控制頂點(diǎn)的約束。(3)當(dāng)六足機(jī)器人處于三足支撐階段時(shí)機(jī)體為3-URR并聯(lián)機(jī)構(gòu),利用旋量理論和指數(shù)積方法建立其正運(yùn)動(dòng)學(xué)模型,利用消元理論和Paden-Kahan子問(wèn)題方法建立其逆運(yùn)動(dòng)學(xué)模型,為進(jìn)行六足機(jī)器人并聯(lián)動(dòng)力學(xué)分析和控制打下基礎(chǔ)。(4)利用Kane動(dòng)力學(xué)分析方法,建立了機(jī)器人單個(gè)可變形腿的逆動(dòng)力學(xué)模型;可用于單個(gè)可變形腿運(yùn)動(dòng)控制器的設(shè)計(jì);分析了關(guān)節(jié)轉(zhuǎn)動(dòng)速度和轉(zhuǎn)動(dòng)加速度對(duì)驅(qū)動(dòng)力矩產(chǎn)生的影響,并分析了各關(guān)節(jié)驅(qū)動(dòng)力矩中慣性力矩、向心力矩、哥氏力矩及重力矩所占的比例,分析結(jié)果有助于進(jìn)一步規(guī)劃?rùn)C(jī)構(gòu)的運(yùn)動(dòng),優(yōu)化機(jī)構(gòu)設(shè)計(jì)和選擇適當(dāng)功率的電機(jī)。(5)設(shè)計(jì)了計(jì)算力矩加RBF神經(jīng)網(wǎng)絡(luò)復(fù)合控制器,其中計(jì)算力矩是已建模的部分,神經(jīng)網(wǎng)絡(luò)補(bǔ)償動(dòng)力學(xué)模型中未建模部分、結(jié)構(gòu)參數(shù)的測(cè)量誤差以及外部擾動(dòng)。采用仿真和實(shí)驗(yàn)對(duì)腿部的軌跡跟蹤控制進(jìn)行了驗(yàn)證,結(jié)果表明了該控制器跟蹤精度高,控制性能優(yōu)良。
[Abstract]:In recent years, in the fields of interstellar exploration, rescue and relief of complex terrain, submarine operations, field monitoring, military reconnaissance and other special dangerous operations, the tasks are becoming more and more difficult and are not suitable for human beings to complete. This has prompted people to continue to study robot technology, using robots to replace human to complete the arduous task. In this paper, the kinematics, dynamics, motion control and kinematics of a new six-legged bionic robot with deformed joints are studied. The main work is as follows: (1) the kinematics model of leg is established by spinor theory and exponential product method, and the explicit inverse kinematics solution is given. The explicit inverse kinematics solution is calculated by combining classical elimination theory and Paden-Kahan subproblem method. The Jacobian matrix of swinging leg is calculated based on spinor method, and the simulation results verify its correctness. (2) in order to improve the stability of leg control of six-legged robot and the speed of action, the joint space trajectory planning is carried out by using the five-degree B-spline curve. Thus, the velocity, acceleration and pulsation of joint motion are continuous. By using the convexity of B-spline curve, the curve constraint of the velocity and acceleration of the leg joint is transformed into the constraint on the control point of the curve. (3) when the six-legged robot is in the three-legged support stage, the body is a parallel mechanism of 3-URR. The positive kinematics model is established by spinor theory and exponential product method, and the inverse kinematics model is established by means of elimination theory and Paden-Kahan subproblem method. It lays a foundation for parallel dynamics analysis and control of hexapod robot. (4) the inverse dynamic model of a single deformable leg of a robot is established by using Kane dynamic analysis method. It can be used in the design of single deformable leg motion controller. The effect of rotation speed and acceleration on the driving torque is analyzed, and the proportion of inertia moment, centripetal force moment, Coriolis moment and weight moment in the drive moment of each joint is analyzed. The analysis results are helpful to further plan the movement of the mechanism, optimize the mechanism design and select the motor of appropriate power. (5) the compound controller of calculating torque and RBF neural network is designed, in which the calculated torque is the part of the model. The unmodeled part of the neural network compensates the dynamic model, the measurement error of the structural parameters and the external disturbance. The trajectory tracking control of the legs is verified by simulation and experiment. The results show that the tracking accuracy of the controller is high and the control performance is good.
【學(xué)位授予單位】：河北工業(yè)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP242

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