Fixed examples for v0.8.0

jhavl · jhavl · commit 74c9bdcc2691 · 2021-02-08T20:32:41.000+10:00
diff --git a/examples/baur.py b/examples/baur.py
@@ -89,4 +89,4 @@
     panda.qd[:n] = qd[:n]
 
     # Step the simulator by 50 ms
-    env.step(50)
+    env.step(0.05)
diff --git a/examples/neo.py b/examples/neo.py
@@ -108,8 +108,8 @@
         # object on the robot to the collision in the scene
         c_Ain, c_bin = panda.link_collision_damper(
             collision, panda.q[:n], 0.3, 0.05, 1.0,
-            startlink=panda.link_dict['panda_link1'],
-            endlink=panda.link_dict['panda_hand'])
+            start=panda.link_dict['panda_link1'],
+            end=panda.link_dict['panda_hand'])
 
         # If there are any parts of the robot within the influence distance
         # to the collision in the scene
diff --git a/examples/park.py b/examples/park.py
@@ -67,4 +67,4 @@
     panda.qd[:n] = qd[:n]
 
     # Step the simulator by 50 ms
-    env.step(50)
+    env.step(0.05)
diff --git a/examples/swift_recording.py b/examples/swift_recording.py
@@ -57,7 +57,7 @@
 while not arrived:
 
     # The pose of the Panda's end-effector
-    Te = panda.fkine()
+    Te = panda.fkine(panda.q)
 
     # Transform from the end-effector to desired pose
     eTep = Te.inv() * Tep
diff --git a/examples/vellipse.py b/examples/vellipse.py
@@ -30,8 +30,8 @@
 while not arrived:
     start = time.time()
 
-    v, arrived = rp.p_servo(panda.fkine(), Tep, 0.5)
-    panda.qd = np.linalg.pinv(panda.jacobe()) @ v
+    v, arrived = rp.p_servo(panda.fkine(panda.q), Tep, 0.5)
+    panda.qd = np.linalg.pinv(panda.jacobe(panda.q)) @ v
     env.step(50)
     stop = time.time()
 
diff --git a/roboticstoolbox/robot/ERobot.py b/roboticstoolbox/robot/ERobot.py
@@ -1847,7 +1847,7 @@ def indiv_calculation(link, link_col, q):
 
                 Je = self.jacobe(
                     q, start=self.base_link, end=link,
-                    offset=link_col.base)
+                    tool=link_col.base)
                 n_dim = Je.shape[1]
                 dp = norm_h @ shape.v
                 l_Ain = np.zeros((1, n))
