update numpy

goal_distance_data.txt

@@ -0,0 +1,18 @@
+2.031290700207774233e-02
+1.765916347503662109e+00
+2.040656628445848553e-02
+3.924012184143066406e+00
+2.002831476383618720e-02
+6.620127439498901367e+00
+2.016944935839263750e-02
+8.746846914291381836e+00
+2.003081774458192674e-02
+1.085422492027282715e+01
+2.016879848002731471e-02
+1.257592725753784180e+01
+2.003082672150304291e-02
+1.468001270294189453e+01
+2.016879701623289223e-02
+1.637891817092895508e+01
+2.003082675435431670e-02
+1.844886445999145508e+01

rps/examples/barrier_certificates/si_barriers_with_boundary.py

@@ -8,14 +8,14 @@
 
 # Instantiate Robotarium object
 N = 5
-initial_conditions = np.array(np.mat('1 0.5 -0.5 0 0.28; 0.8 -0.3 -0.75 0.1 0.34; 0 0 0 0 0'))
+initial_conditions = np.array(np.asmatrix('1 0.5 -0.5 0 0.28; 0.8 -0.3 -0.75 0.1 0.34; 0 0 0 0 0'))
 r = robotarium.Robotarium(number_of_robots=N, show_figure=True, initial_conditions=initial_conditions,sim_in_real_time=True)
 
 # The robots will never reach their goal points so set iteration number
 iterations = 3000
 
 # Define goal points outside of the arena
-goal_points = np.array(np.mat('5 -5 5 -5 5; 5 5 -5 -5 5; 0 0 0 0 0'))
+goal_points = np.array(np.asmatrix('5 -5 5 -5 5; 5 5 -5 -5 5; 0 0 0 0 0'))
 
 # Create unicycle position controller
 si_position_controller = create_si_position_controller()

rps/examples/barrier_certificates/uni_barriers_with_boundary.py

@@ -15,7 +15,7 @@
 iterations = 3000
 
 # Define goal points outside of the arena
-goal_points = np.array(np.mat('5 5 5 5 5; 5 5 5 5 5; 0 0 0 0 0'))
+goal_points = np.array(np.asmatrix('5 5 5 5 5; 5 5 5 5 5; 0 0 0 0 0'))
 
 # Create unicycle position controller
 unicycle_position_controller = create_clf_unicycle_position_controller()

rps/examples/barrier_certificates/uni_dd_barriers_with_boundary.py

@@ -15,7 +15,7 @@
 iterations = 3000
 
 # Define goal points outside of the arena
-goal_points = np.array(np.mat('5 5 5 5 5; 5 5 5 5 5; 0 0 0 0 0'))
+goal_points = np.array(np.asmatrix('5 5 5 5 5; 5 5 5 5 5; 0 0 0 0 0'))
 
 # Create unicycle position controller
 unicycle_position_controller = create_clf_unicycle_position_controller()

rps/examples/go_to_point/si_go_to_point.py

@@ -9,7 +9,7 @@
 
 # Instantiate Robotarium object
 N = 5
-initial_conditions = np.array(np.mat('1 0.5 -0.5 0 0.28; 0.8 -0.3 -0.75 0.1 0.34; 0 0 0 0 0'))
+initial_conditions = np.array(np.asmatrix('1 0.5 -0.5 0 0.28; 0.8 -0.3 -0.75 0.1 0.34; 0 0 0 0 0'))
 
 r = robotarium.Robotarium(number_of_robots=N, show_figure=True, initial_conditions=initial_conditions, sim_in_real_time=False)
 

rps/examples/go_to_point/uni_go_to_point.py

@@ -9,7 +9,7 @@
 
 # Instantiate Robotarium object
 N = 5
-initial_conditions = np.array(np.mat('1 0.5 -0.5 0 0.28; 0.8 -0.3 -0.75 0.1 0.34; 0 0 0 0 0'))
+initial_conditions = np.array(np.asmatrix('1 0.5 -0.5 0 0.28; 0.8 -0.3 -0.75 0.1 0.34; 0 0 0 0 0'))
 r = robotarium.Robotarium(number_of_robots=N, show_figure=True, initial_conditions=initial_conditions,sim_in_real_time=True)
 
 # Define goal points by removing orientation from poses

rps/examples/go_to_pose/uni_go_to_pose_clf.py

@@ -9,7 +9,7 @@
 
 # Instantiate Robotarium object
 N = 5
-initial_conditions = np.array(np.mat('1 0.5 -0.5 0 0.28; 0.8 -0.3 -0.75 0.1 0.34; 0 0 0 0 0'))
+initial_conditions = np.array(np.asmatrix('1 0.5 -0.5 0 0.28; 0.8 -0.3 -0.75 0.1 0.34; 0 0 0 0 0'))
 r = robotarium.Robotarium(number_of_robots=N, show_figure=True, initial_conditions=initial_conditions,sim_in_real_time=True)
 
 # Define goal points by removing orientation from poses

rps/examples/go_to_pose/uni_go_to_pose_hybrid.py

@@ -9,7 +9,7 @@
 
 # Instantiate Robotarium object
 N = 5
-initial_conditions = np.array(np.mat('1 0.5 -0.5 0 0.28; 0.8 -0.3 -0.75 0.1 0.34; 0 0 0 0 0'))
+initial_conditions = np.array(np.asmatrix('1 0.5 -0.5 0 0.28; 0.8 -0.3 -0.75 0.1 0.34; 0 0 0 0 0'))
 r = robotarium.Robotarium(number_of_robots=N, show_figure=True, initial_conditions=initial_conditions,sim_in_real_time=True)
 
 # Define goal points by removing orientation from poses

rps/examples/plotting/uni_go_to_pose_hybrid_with_plotting.py

@@ -9,7 +9,7 @@
 
 # Instantiate Robotarium object
 N = 5
-initial_conditions = np.array(np.mat('1 0.5 -0.5 0 0.28; 0.8 -0.3 -0.75 0.1 0.34; 0 0 0 0 0'))
+initial_conditions = np.array(np.asmatrix('1 0.5 -0.5 0 0.28; 0.8 -0.3 -0.75 0.1 0.34; 0 0 0 0 0'))
 r = robotarium.Robotarium(number_of_robots=N, show_figure=True, initial_conditions=initial_conditions,sim_in_real_time=True)
 
 # Define goal points by removing orientation from poses

rps/utilities/barrier_certificates.py

@@ -410,17 +410,17 @@ def create_unicycle_differential_drive_barrier_certificate(max_num_obstacle_poin
     projection_distance =0.05, barrier_gain = 150, safety_radius = 0.17):
 
 
-    D = np.matrix([[wheel_radius/2, wheel_radius/2], [-wheel_radius/base_length, wheel_radius/base_length]])
-    L = np.matrix([[1,0],[0,projection_distance]])* D
-    disturb = np.matrix([[-disturbance, -disturbance, disturbance, disturbance],[-disturbance, disturbance, disturbance, -disturbance]])
+    D = np.asmatrixrix([[wheel_radius/2, wheel_radius/2], [-wheel_radius/base_length, wheel_radius/base_length]])
+    L = np.asmatrixrix([[1,0],[0,projection_distance]])* D
+    disturb = np.asmatrixrix([[-disturbance, -disturbance, disturbance, disturbance],[-disturbance, disturbance, disturbance, -disturbance]])
     num_disturbs = np.size(disturb[1,:])
 
     max_num_constraints = (max_num_robots**2-max_num_robots)//2 + max_num_robots*max_num_obstacle_points
-    A = np.matrix(np.zeros([max_num_constraints, 2*max_num_robots]))
-    b = np.matrix(np.zeros([max_num_constraints, 1]))
-    Os = np.matrix(np.zeros([2,max_num_robots]))
-    ps = np.matrix(np.zeros([2,max_num_robots]))
-    Ms = np.matrix(np.zeros([2,2*max_num_robots]))
+    A = np.asmatrixrix(np.zeros([max_num_constraints, 2*max_num_robots]))
+    b = np.asmatrixrix(np.zeros([max_num_constraints, 1]))
+    Os = np.asmatrixrix(np.zeros([2,max_num_robots]))
+    ps = np.asmatrixrix(np.zeros([2,max_num_robots]))
+    Ms = np.asmatrixrix(np.zeros([2,2*max_num_robots]))
 
     def robust_barriers(dxu, x, obstacles=np.empty(0)):
 
@@ -464,7 +464,7 @@ def robust_barriers(dxu, x, obstacles=np.empty(0)):
             diffs = ps[:,i] - ps[:, i+1:num_robots]
             hs = np.sum(np.square(diffs),0) - safety_radius**2 # 1 by N
             h_dot_is = 2*diffs.T*MDs[:,(2*i, 2*i+1)] # N by 2
-            h_dot_js = np.matrix(np.zeros((2,num_robots - (i+1))))
+            h_dot_js = np.asmatrixrix(np.zeros((2,num_robots - (i+1))))
             h_dot_js[0, :] = -np.sum(2*np.multiply(diffs, MDs[:,2*(i+1):2*num_robots:2]), 0)
             h_dot_js[1, :] = -np.sum(2*np.multiply(diffs, MDs[:,2*(i+1)+1:2*num_robots:2]), 0)
             new_constraints = num_robots - i - 1
@@ -496,7 +496,7 @@ def robust_barriers(dxu, x, obstacles=np.empty(0)):
         # Solve QP program generated earlier
         L_all = np.kron(np.eye(num_robots), L)
         dxu = np.linalg.inv(D)*dxu # Convert user input to differential drive
-        vhat = np.matrix(np.reshape(dxu ,(2*num_robots,1), order='F'))
+        vhat = np.asmatrixrix(np.reshape(dxu ,(2*num_robots,1), order='F'))
         H = 2*L_all.T*L_all
         f = np.transpose(-2*np.transpose(vhat)*np.transpose(L_all)*L_all)
 

rps/utilities/barrier_certificates2.py

@@ -9,18 +9,18 @@
 
 def create_robust_barriers(max_num_obstacles = 100, max_num_robots = 30, d = 5, wheel_vel_limit = 12.5, base_length = 0.105, wheel_radius = 0.016,
     projection_distance =0.05, gamma = 150, safety_radius = 0.12): # gamma was 150
-    D = np.matrix([[wheel_radius/2, wheel_radius/2], [-wheel_radius/base_length, wheel_radius/base_length]])
-    L = np.matrix([[1,0],[0,projection_distance]])* D
-    disturb = np.matrix([[-d, -d, d, d],[-d, d, d, -d]])
+    D = np.asmatrixrix([[wheel_radius/2, wheel_radius/2], [-wheel_radius/base_length, wheel_radius/base_length]])
+    L = np.asmatrixrix([[1,0],[0,projection_distance]])* D
+    disturb = np.asmatrixrix([[-d, -d, d, d],[-d, d, d, -d]])
     num_disturbs = np.size(disturb[1,:])
 
     #TODO: Take out 4*max_num_robots?
     max_num_constraints = (max_num_robots**2-max_num_robots)//2 + max_num_robots*max_num_obstacles + 4*max_num_robots
-    A = np.matrix(np.zeros([max_num_constraints, 2*max_num_robots]))
-    b = np.matrix(np.zeros([max_num_constraints, 1]))
-    Os = np.matrix(np.zeros([2,max_num_robots]))
-    ps = np.matrix(np.zeros([2,max_num_robots]))
-    Ms = np.matrix(np.zeros([2,2*max_num_robots]))
+    A = np.asmatrixrix(np.zeros([max_num_constraints, 2*max_num_robots]))
+    b = np.asmatrixrix(np.zeros([max_num_constraints, 1]))
+    Os = np.asmatrixrix(np.zeros([2,max_num_robots]))
+    ps = np.asmatrixrix(np.zeros([2,max_num_robots]))
+    Ms = np.asmatrixrix(np.zeros([2,2*max_num_robots]))
 
     def robust_barriers(dxu, x, obstacles):
 
@@ -68,7 +68,7 @@ def robust_barriers(dxu, x, obstacles):
             diffs = ps[:,i] - ps[:, i+1:num_robots]
             hs = np.sum(np.square(diffs),0) - safety_radius**2 # 1 by N
             h_dot_is = 2*diffs.T*MDs[:,2*i:2*i+2] # N by 2
-            h_dot_js = np.matrix(np.zeros((2,num_robots - (i+1))))
+            h_dot_js = np.asmatrixrix(np.zeros((2,num_robots - (i+1))))
             h_dot_js[0, :] = -np.sum(2*np.multiply(diffs, MDs[:,2*(i+1):2*num_robots:2]), 0)
             h_dot_js[1, :] = -np.sum(2*np.multiply(diffs, MDs[:,2*(i+1)+1:2*num_robots:2]), 0)
             new_constraints = num_robots - i - 1
@@ -100,7 +100,7 @@ def robust_barriers(dxu, x, obstacles):
         # Solve QP program generated earlier
         L_all = np.kron(np.eye(num_robots), L)
         dxu = np.linalg.inv(D)*dxu # Convert user input to differential drive
-        vhat = np.matrix(np.reshape(dxu ,(2*num_robots,1), order='F'))
+        vhat = np.asmatrixrix(np.reshape(dxu ,(2*num_robots,1), order='F'))
         H = 2*L_all.T*L_all
         f = np.transpose(-2*np.transpose(vhat)*np.transpose(L_all)*L_all)