本文選題：移動機械臂 + 路徑規(guī)劃�。� 參考：《浙江理工大學(xué)》2017年碩士論文

【摘要】：移動機械臂是由可移動的基座和可操作的機械臂組成的復(fù)雜非線性系統(tǒng)。多自由度的機械臂和可移動的基座讓移動機械臂兼具移動性和靈活性的雙重優(yōu)點,使得其在家庭服務(wù),智能倉儲和工業(yè)生產(chǎn)等領(lǐng)域中得到了廣泛的應(yīng)用。針對移動機械臂理論和應(yīng)用的研究對于提高生活質(zhì)量和促進生產(chǎn)力發(fā)展具有至關(guān)重要的作用,近年來已成為機器人領(lǐng)域的研究熱點。本文針對移動機械臂運動學(xué)范疇中的若干問題進行了研究,主要內(nèi)容包括:移動機械臂基座的路徑規(guī)劃、六自由度機械臂的逆運動學(xué)問題和機械臂避障軌跡規(guī)劃的優(yōu)化問題。本文的創(chuàng)新點主要體現(xiàn)在以下三個方面:(1)針對人工勢場法(APF)容易產(chǎn)生局部極小值和路徑規(guī)劃效率不高的問題,提出一種基于切向量和粒子群優(yōu)化的改進人工勢場法(PSO-TVAPF)。首先,迭代的計算的機器人當前位置與目標之間障礙物的切向量,并通過一定的篩選策略選擇最優(yōu)切向量;然后,將所得切向量與傳統(tǒng)人工勢場法中的引力和斥力按照某種比例進行合成,形成機器人路徑規(guī)劃的驅(qū)動力,切向量的引入,對于避免局部極小值和改善路徑規(guī)劃質(zhì)量有著顯著的作用;最后,為了進一步提高算法的魯棒性和路徑規(guī)劃效率,使用粒子群算法對基于切向量的人工勢場法(TVAPF)進行優(yōu)化。仿真實驗和實物實驗表明本文提出的基于切向量和粒子群優(yōu)化的人工勢場法能夠有效的避免局部極小值和大幅度的縮短最終路徑長度。(2)提出一種基于極限學(xué)習(xí)機和順序變異的遺傳算法優(yōu)化的計算六自由度機械臂逆運動學(xué)解的智能算法(ELM-SGA)。算法的基本思路是先利用極限學(xué)習(xí)機求出一個精度不高的初始逆解,然后利用基于順序變異的遺傳算法優(yōu)化初始逆解得到高精度解。ELM-SGA算法的提出受到基于神經(jīng)網(wǎng)絡(luò)和遺傳算法求逆解的混合智能算法(Hybrid)的啟發(fā),在保證與原算法達到同等精度的情況下最大限度的提高算法的時間效率,這里的時間效率包括兩個方面,神經(jīng)網(wǎng)絡(luò)的訓(xùn)練時間和計算逆解的時間。極限學(xué)習(xí)機隨機初始化輸入層權(quán)值和隱藏偏置能夠最大限度提高訓(xùn)練速度。與傳統(tǒng)的遺傳算法的隨機變異不同,本文提出一種順序變異的方式對初步逆解進行優(yōu)化,這能夠有效的提高遺傳算法的局部搜索能力,提高算法收斂速度。仿真實驗和MT-ARM機械臂的驗證也證實了本文提出的算法在保證高精度的前提下能有效提高求機械臂運動學(xué)逆解的時間效率。(3)針對機械臂的避障軌跡規(guī)劃問題,提出一種改進的人工勢場法,然后利用本文提出的逆解算法ELM-SGA對軌跡上的點進行求逆,進行碰撞檢測,直至得到安全軌跡為止,最后利用粒子群算法以末端軌跡長度和機械臂能耗為適應(yīng)度函數(shù)對軌跡進行優(yōu)化。仿真實驗結(jié)果表明,改進后的人工勢場法能夠有效縮短末端軌跡長度和降低機械臂能耗。
[Abstract]:A mobile manipulator is a complex nonlinear system composed of movable base and manipulable manipulator. The multi degree of freedom manipulator and movable base make the mobile arm with both mobility and flexibility, making it widely used in the field of home service, intelligent storage and industrial production. The research on the theory and application of the robot arm plays a vital role in improving the quality of life and promoting the development of the productive forces. In recent years, it has become a hot topic in the field of robotics. This paper has studied several problems in the kinematic category of the mobile manipulator, including the path planning of the base of the mobile manipulator, six self The inverse kinematics problem of the degree manipulator and the optimization of the trajectory planning of the manipulator obstacle avoidance are mainly embodied in the following three aspects: (1) an improved artificial potential field method based on the tangent vector and particle swarm optimization (PS) is proposed for the problem that the artificial potential method (APF) is easy to produce the local minimum and the path planning efficiency is not high. O-TVAPF). First, the iterative calculation of the current position of the robot and the tangent vector of the obstacle between the target, and select the optimal tangent vector by a certain filtering strategy. Then, the gravitational and repulsion of the tangent vector and the traditional artificial potential field method are synthesized to form the driving force of the robot path planning, and the tangent vector is formed. In the end, in order to further improve the robustness of the algorithm and the efficiency of path planning, the particle swarm optimization is used to optimize the artificial potential field (TVAPF) method based on the tangent vector. The simulation experiment and the physical experiment show that the tangent vector and particle are proposed in this paper. The artificial potential method of subgroup optimization can effectively avoid local minimum and reduce the length of the final path. (2) an intelligent algorithm (ELM-SGA) is proposed to calculate the inverse kinematics solution of the six degree of freedom manipulator based on the limit learning machine and the genetic algorithm of sequence variation. The basic idea of the algorithm is to use the limit learning machine first. An initial inverse solution with low precision is produced, and then a high precision solution.ELM-SGA algorithm is obtained by optimizing the initial inverse solution based on the genetic algorithm based on the sequence variation. It is inspired by the hybrid intelligent algorithm (Hybrid), which is based on the neural network and the genetic algorithm for the inverse solution. The time efficiency of the high algorithm, the time efficiency here includes two aspects, the training time of the neural network and the time of calculating the inverse solution. The random initialization of the input layer weight and the hidden bias of the limit learning machine can maximize the training speed. This paper proposes a sequential variation method in the random variation of the traditional genetic algorithm. The initial inverse solution is optimized, which can effectively improve the local search ability of the genetic algorithm and improve the convergence speed of the algorithm. The simulation experiment and the verification of the MT-ARM manipulator also prove that the algorithm proposed in this paper can effectively improve the time efficiency of solving the inverse kinematics of the manipulator. (3) the obstacle avoidance for the manipulator. In trajectory planning, an improved artificial potential method is proposed, and then the inverse algorithm ELM-SGA is used to reverse the points on the trajectory, and the collision detection is carried out until the security trajectory is obtained. Finally, the particle swarm algorithm is used to optimize the trajectory with the length of the end trajectory and the energy consumption of the manipulator as the fitness function. The experimental results show that the improved artificial potential field method can effectively shorten the length of the end trajectory and reduce the energy consumption of the robot arm.

【學(xué)位授予單位】：浙江理工大學(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：1784555