Goutham Bandaru

Question_3.py

@@ -0,0 +1,47 @@
+#DH parameters and endeffector properties for a STANFORD Type manipultor
+
+import numpy as np
+n =  4  #number of links
+
+#defing Cos and Sin as functions for simplicity.
+def c(t):
+    c= np.cos(t*np.pi/180)
+    return c
+
+def s(t):
+    s= np.sin(t*np.pi/180)
+    return s
+
+# DH parameters for SCARA manipulator
+DH = np.array([[10, 0, 0 , 0],
+               [10, 180, 0, 0],
+               [0, 0, 0, 0,],
+               [0, 0, 5, 0]])
+
+#Initializing the T matrix
+T = np.array([[1,0,0,0],
+              [0,1,0,0],
+              [0,0,1,0],
+              [0,0,0,1]])
+
+B = np.array([[1],[2],[3],[1]]) #given end effector position
+W = np.array([[5],[6],[4],[1]])
+# making the T matrix and matrix multiplying all the H matrices.
+for i in range(0,4):
+    H = np.array([[c(DH[i,3]), -s(DH[i,3])*c(DH[i,1]), s(DH[i,3])*s(DH[i,1]), DH[i,0]*c(DH[i,3])],
+                  [s(DH[i,3]) , c(DH[i,3])*c(DH[i,1]), c(DH[i,3])*s(DH[i,1]), DH[i,0]*s(DH[i,3])],
+                  [0, s(DH[i,1]), c(DH[i,1]), DH[i,1]],
+                  [0, 0, 0, 1]])
+    T = np.dot(T,H)
+
+# T is the Jacobian manipulator.
+
+print('Manipulator Jacobian is')
+print(T)
+E = np.dot(T, B)
+print('EndEffector position is ', E[0],E[1],E[2] )
+#E gives the end effector postion with respect to base frame.
+
+V = np.dot(T,W)
+print('EndEffector velocity is ', V[0],V[1],V[2] )
+# V gives the end effector velocity with respect to base frame

Question_4.py

@@ -0,0 +1,50 @@
+#DH parameters and endeffector properties for a STANFORD Type manipultor
+
+import numpy as np
+n =  6  #number of links
+
+#defing Cos and Sin as functions for simplicity.
+def c(t):
+    c= np.cos(t*np.pi/180)
+    return c
+
+def s(t):
+    s= np.sin(t*np.pi/180)
+    return s
+
+# DH parameters for STANFORD manipulator
+DH = np.array([[0, -90, 0 , 0],
+               [0, 90, 10, 0],
+               [0, 0, 0, 0,],
+               [0, -90, 0, 0],
+               [0, 90, 0, 0],
+               [0, 0, 10, 0]])
+
+#Initializing the T matrix
+T = np.array([[1,0,0,0],
+              [0,1,0,0],
+              [0,0,1,0],
+              [0,0,0,1]])
+
+B = np.array([[1],[2],[3],[1]]) #given end effector position
+W = np.array([[5],[6],[4],[1]])
+# making the T matrix and matrix multiplying all the H matrices.
+for i in range(0,4):
+    H = np.array([[c(DH[i,3]), -s(DH[i,3])*c(DH[i,1]), s(DH[i,3])*s(DH[i,1]), DH[i,0]*c(DH[i,3])],
+                  [s(DH[i,3]) , c(DH[i,3])*c(DH[i,1]), c(DH[i,3])*s(DH[i,1]), DH[i,0]*s(DH[i,3])],
+                  [0, s(DH[i,1]), c(DH[i,1]), DH[i,1]],
+                  [0, 0, 0, 1]])
+    T = np.dot(T,H)
+
+# T is the Jacobian manipulator.
+
+print('Manipulator Jacobian is')
+print(T)
+
+E = np.dot(T, B)
+print('EndEffector position is ', E[0],E[1],E[2] )
+#E gives the end effector postion with respect to base frame.
+
+V = np.dot(T,W)
+print('EndEffector velocity is ', V[0],V[1],V[2] )
+# V gives the end effector velocity with respect to base frame