V-rep學習筆記：機器人逆運動學數值解法（Damped Least Squares / Levenberg-Marquardt Method）

　　The damped least squares method is also called the Levenberg-Marquardt method. Levenberg-Marquardt算法是最優化算法中的一種。它是使用最廣泛的非線性最小二乘算法，具有梯度法和牛頓法的優點。當λ很小時，步長等於牛頓法步長，當λ很大時，步長約等於梯度下降法的步長。

　　The damped least squares method can be theoretically justified as follows.Rather than just finding the minimum vector ∆θ that gives a best solution to equation （pseudo inverse method就是求的極小范數解）, we find the value of ∆θ that minimizes the quantity：

where λ ∈ R is a non-zero damping constant. This is equivalent to minimizing the quantity：

The corresponding normal equation is（根據矩陣論簡明教程P83 最小二乘問題：設A∈R^m×n，b∈R^m. 若x₀∈Rⁿ是Ax=b的最小二乘解，則x₀是方程組A^TAx=A^Tb的解，稱該式為Ax=b的法方程組.）

This can be equivalently rewritten as：

It can be shown that J^TJ + λ²I is non-singular when λ is appropriate（選取適當的參數λ可以保證矩陣J^TJ + λ²I非奇異）. Thus, the damped least squares solution is equal to：

Now J^TJ  is an n × n matrix, where n is the number of degrees of freedom. It is easy to find that (J^TJ + λ²I)⁻¹J^T= J^T (JJ^T + λ²I)⁻¹(等式兩邊同乘(J^TJ + λ²I)進行恆等變形). Thus：

The advantage of the equation is that the matrix being inverted is only m×m where m = 3k is the dimension of the space of target positions, and m is often much less than n. Additionally, the equation can be computed without needing to carry out the matrix inversion, instead row operations can find f such that (JJ^T + λ²I) f = e and then J^Tf is the solution. The damping constant depends on the details of the multibody and the target positions and must be chosen carefully to make equation numerically stable. The damping constant should large enough so that the solutions for ∆θ are well-behaved near singularities, but if it is chosen too large, then the convergence rate is too slow. 

　　以平面二連桿機構為例，使用同樣的V-rep模型，將目標點放置在接近機構奇異位置處，使用DLS方法求逆解。在下面的Python程序中關節角初始值就給在奇異點上，可以看出最終DLS算法還是能收斂，而pseudo inverse方法在奇異點處就無法收斂。The damped least squares method avoids many of the pseudo inverse method’s problems with singularities and can give a numerically stable method of selecting ∆θ

import vrep             #V-rep library
import sys      
import time
import math  
import numpy as np

# Starts a communication thread with the server (i.e. V-REP). 
clientID=vrep.simxStart('127.0.0.1', 20001, True, True, 5000, 5)

# clientID: the client ID, or -1 if the connection to the server was not possible
if clientID!=-1:  #check if client connection successful
    print 'Connected to remote API server'
else:
    print 'Connection not successful'
    sys.exit('Could not connect')    # Exit from Python


# Retrieves an object handle based on its name.
errorCode,J1_handle = vrep.simxGetObjectHandle(clientID,'j1',vrep.simx_opmode_oneshot_wait)
errorCode,J2_handle = vrep.simxGetObjectHandle(clientID,'j2',vrep.simx_opmode_oneshot_wait)
errorCode,target_handle = vrep.simxGetObjectHandle(clientID,'target',vrep.simx_opmode_oneshot_wait)
errorCode,consoleHandle = vrep.simxAuxiliaryConsoleOpen(clientID,'info',5,1+4,None,None,None,None,vrep.simx_opmode_oneshot_wait)

uiHandle = -1
errorCode,uiHandle = vrep.simxGetUIHandle(clientID,"UI", vrep.simx_opmode_oneshot_wait)
buttonEventID = -1
err,buttonEventID,aux = vrep.simxGetUIEventButton(clientID,uiHandle,vrep.simx_opmode_streaming)


L1 = 0.5        # link length
L2 = 0.5
lamda = 0.2     # damping constant
stol = 1e-2     # tolerance
nm = 100        # initial error
count = 0       # iteration count
ilimit = 1000   # maximum iteration


# initial joint value
# note that workspace-boundary singularities occur when q2 approach 0 or 180 degree
q = np.array([0,0])   


while True:
    retcode, target_pos = vrep.simxGetObjectPosition(clientID, target_handle, -1, vrep.simx_opmode_streaming)

    if(nm > stol):
        vrep.simxAuxiliaryConsolePrint(clientID, consoleHandle, None, vrep.simx_opmode_oneshot_wait) # "None" to clear the console window

        x = np.array([L1*math.cos(q[0])+L2*math.cos(q[0]+q[1]), L1*math.sin(q[0])+L2*math.sin(q[0]+q[1])])
        error = np.array([target_pos[0],target_pos[1]]) - x

        J = np.array([[-L1*math.sin(q[0])-L2*math.sin(q[0]+q[1]), -L2*math.sin(q[0]+q[1])],\
                      [L1*math.cos(q[0])+L2*math.cos(q[0]+q[1]), L2*math.cos(q[0]+q[1])]])

        f = np.linalg.solve(J.dot(J.transpose())+lamda**2*np.identity(2), error)
        
        dq = np.dot(J.transpose(), f)
        q = q + dq

        nm = np.linalg.norm(error)

        count = count + 1
        if count > ilimit:
            vrep.simxAuxiliaryConsolePrint(clientID,consoleHandle,"Solution wouldn't converge\r\n",vrep.simx_opmode_oneshot_wait)
        vrep.simxAuxiliaryConsolePrint(clientID,consoleHandle,'q1:'+str(q[0]*180/math.pi)+' q2:'+str(q[1]*180/math.pi)+'\r\n',vrep.simx_opmode_oneshot_wait)  
        vrep.simxAuxiliaryConsolePrint(clientID,consoleHandle,str(count)+' iterations'+'  err:'+str(nm)+'\r\n',vrep.simx_opmode_oneshot_wait)   


    err, buttonEventID, aux = vrep.simxGetUIEventButton(clientID,uiHandle,vrep.simx_opmode_buffer)    
    if ((err==vrep.simx_return_ok) and (buttonEventID == 1)):
        '''A button was pressed/edited/changed. React to it here!'''
        vrep.simxSetJointPosition(clientID,J1_handle, q[0]+math.pi/2, vrep.simx_opmode_oneshot )  
        vrep.simxSetJointPosition(clientID,J2_handle, q[1], vrep.simx_opmode_oneshot )
        
        '''Enable streaming again (was automatically disabled with the positive event):'''
        err,buttonEventID,aux=vrep.simxGetUIEventButton(clientID,uiHandle,vrep.simx_opmode_streaming)

    time.sleep(0.01)

 

參考：

“逆運動學”——從操作空間到關節空間（上篇）