alpaqa 0.0.1
Nonconvex constrained optimization
Namespaces | Functions | Variables
bicycle-obstacle-avoidance-mpc.py File Reference

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Namespaces

namespace  bicycle-obstacle-avoidance-mpc
 

Functions

def solve_ocp (state, y_sol, x_sol)
 

Variables

float Ts = 0.05
 
int N_hor = 18
 
int N_sim = 80
 
bool multipleshooting = False
 
int R_obstacle = 2
 
 f
 
 nlp
 
 bounds
 
 n_states
 
 n_inputs
 
 first_input_idx
 
string name = "mpcproblem"
 
 f_prob = cs.Function("f", [nlp["x"], nlp["p"]], [nlp["f"]])
 
 g_prob = cs.Function("g", [nlp["x"], nlp["p"]], [nlp["g"]])
 
 prob = pa.generate_and_compile_casadi_problem(f_prob, g_prob, name=name)
 
 lowerbound
 
 upperbound
 
int lbfgsmem = N_hor
 
int tol = 1e-5
 
bool verbose = False
 
dictionary panocparams
 
 innersolver
 
 almparams
 
 solver = pa.ALMSolver(almparams, innersolver)
 
 state = np.array([-5, 0, 0, 0])
 
 dest = np.array([5, 0.1, 0, 0])
 
 x_sol = np.concatenate((np.tile(state, N_hor), np.zeros((n_inputs * N_hor,))))
 
 y_sol = np.zeros((prob.m,))
 
 xs = np.zeros((N_sim, n_states))
 
 times = np.zeros((N_sim,))
 
 t
 
 stats
 
 input = x_sol[first_input_idx : first_input_idx + n_inputs]
 
 fig_trajectory
 
 ax
 
 c = plt.Circle((0, 0), R_obstacle)
 
 fig_time