Fixed neo example
@@ -49,7 +49,7 @@ env.add(s1)
env.add(target)
# Set the desired end-effector pose to the location of target
-Tep = panda.fkine()
+Tep = panda.fkine(panda.q)
Tep.A[:3, 3] = target.base.t
Tep.A[2, 3] += 0.1
@@ -58,7 +58,7 @@ arrived = False
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
@@ -83,7 +83,7 @@ while not arrived:
Q[n:, n:] = (1 / e) * np.eye(6)
# The equality contraints
- Aeq = np.c_[panda.jacobe(), np.eye(6)]
+ Aeq = np.c_[panda.jacobe(panda.q), np.eye(6)]
beq = v.reshape((6,))
# The inequality constraints for joint limit avoidance
@@ -108,7 +108,8 @@ while not arrived:
# 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,
- panda.link_dict['panda_link1'], panda.link_dict['panda_hand'])
+ startlink=panda.link_dict['panda_link1'],
+ endlink=panda.link_dict['panda_hand'])
# If there are any parts of the robot within the influence distance
# to the collision in the scene
@@ -120,7 +121,7 @@ while not arrived:
bin = np.r_[bin, c_bin]
# Linear component of objective function: the manipulability Jacobian
- c = np.r_[-panda.jacobm().reshape((n,)), np.zeros(6)]
+ c = np.r_[-panda.jacobm(panda.q).reshape((n,)), np.zeros(6)]
# The lower and upper bounds on the joint velocity and slack variable
lb = -np.r_[panda.qdlim[:n], 10 * np.ones(6)]
