Modifying the OCP without recompling it in python interface

User Questions
1

Hi acados team!

Thank you again for all your work on acados.
My question is, what should I do to create a new ocp problem (new initial conditions and new parameters) without recompiling the solver with the python interface?

I am trying to solve a multi-robot motion planning problem with an MPC. Each robot has an initial position and a goal to reach. I would like to solve the MPC for different couples (initial position,goal). For this, I set the goal as a parameter. I export the solver once, solve the first ocp, and then to solve a new ocp, I update the parameter (goal) with ocp_solver.acados_ocp.parameter_values = goal, and I use ocp_solver.solve_for_x0(x0) to use the new initial position. However, the results I get are still different than the ones I get if I recompile the solver.

At each time step, I also initialize the solver and the integrator using set():
ocp_solver.set(0, “lbx”, xcurrent)
ocp_solver.set(0, “ubx”, xcurrent)
ocp_solver.set(0,‘x’,xcurrent)
ocp_solver.set(0,‘u’,np.zeros((nu,)))
ocp_solver.set(0,‘p’, goal)

What instructions do you think I should add? Does the ocp_solver keep the previous solution in memory and is it possible to delete it? I tried the function reset(), but then I get errors of status 4. I have also tried to use store_iterate() and load_iterate() but the problem is still here. I probably do not use these functions correctly? I do store_iterate(“json_file.json”) once after exporting/compiling the solver. And then, during the simulation I do load_iterate(“json_file.json”) at each time step, before initializing the solver, before calling .solve_for_x0. Should I create different json files for each different ocp?

Thank you very much in advance for your help!

2

why do you think the reset() is the issue?
Can you problem just be infeasible?

3

Hi,

Thank you for your quick reply!

I don’t think reset() is the issue because when I solve the problem exporting the solver for the specific initial positions and goals, then it works well. The problem occurs when I want to solve multiple ocp without re-exporting the solver.

4

In this case, reset() should indeed work.
Maybe you can do some debugging:

  • You can use store_iterate() once with your reset and once after exporting the solver for the same initial position and compare the files.
  • If this is indeed the same. You can also carry out a single SQP iteration and run dump_last_qp_to_json() and compare the two.

Let me know what you find!

5

Indeed the first debugging you propose actually reveals the problem!

  • When I apply store_iterate() after exporting the solver, every values are zeros except the x_i values that are equal to the my initial conditions x0. I guess these values correspond to the initial condition constraint.

  • When I apply store_iterate() after reset() then all the values in the json file are equal to zero, the x_i values too.

How could I set again the initial condition?

Thank you again for your help!

6

If I set my initial condition using ocp_solver.set(i,'x',x0) before store_iterate() and now both json files are the same, but the problem is still not solved.

I will try your second suggestion for debugging.

7

I have finally found the solution!

Here are the steps I had to do:

  • ocp_solver.reset()

  • Initialize the solver, so the initial constraint, initial values of my state, and initial value of the goal parameter:

 for i in range(Npred+1):
        ocp_solver.set(i,'x',x0)
        ocp_solver.set(i,'p', goal)
 ocp_solver.set(0, "lbx", x0)
 ocp_solver.set(0, "ubx", x0)

I especially needed to do it until Npred+1 and not Npred. Otherwise, my cost was NaN at the end of the horizon, since my goal is directly used in my cost function.

  • ocp_solver.store_iterate(json_file)

  • Then, in the closed-loop simulation, at each timestep:

ocp_solver.load_iterate(json_file)
ocp_solver.set(0,'x',xcurrent)
ocp_solver.set(0,'u',np.zeros((nu,)))
ocp_solver.set(0,'p', goal)
ocp_solver.solve_for_x0(xcurrent)

I was actually stuck on this problem for a long time, so thank you so much for your precious help and amazing responsiveness!
Your suggested tricks will definitely be useful for any problem I face in the future.

Best,
Manohari

1 Like
8

Hi Manohari,

I am happy it helped and you were able to solve your issue! :slight_smile:

I guess only this line should not be necessary, if the control values in the iterate you load are already 0.

ocp_solver.set(0,'u',np.zeros((nu,)))

Best,
Jonathan