My initial code is too long to put it here, so I will use a simple case but has the same problem instead.
I use VehicleModels.jl and NLOptControl.jl in this code. I set the goal as 70 0 in this scenario. But
c = Dict("goal"=>Dict("x"=>70.,"yVal"=>0.,"tol"=>0.1), "obstacle"=>Dict("x0"=>[25,55],"y0"=>[0,0],"vx"=>[0.,0.],"vy"=>[0.,0.],"radius"=>[1,1]),"tolerances"=>Dict("fx"=>NaN,"fy"=>NaN))
The goal here is not a hard constrain. I have to put the final destination in the XF which I now set to be NaN.
But if I do that, an error would show that the New columns must have the same length as old columns.
It's a simple case, it definitely has a optimal solution.
If I didn't include it inside the XF, the vehicle wouldn't move. And if I include it in XF, the error showed up
My initial code is too long to put it here, so I will use a simple case but has the same problem instead.
I use VehicleModels.jl and NLOptControl.jl in this code. I set the goal as 70 0 in this scenario. But
c = Dict("goal"=>Dict("x"=>70.,"yVal"=>0.,"tol"=>0.1), "obstacle"=>Dict("x0"=>[25,55],"y0"=>[0,0],"vx"=>[0.,0.],"vy"=>[0.,0.],"radius"=>[1,1]),"tolerances"=>Dict("fx"=>NaN,"fy"=>NaN))
The goal here is not a hard constrain. I have to put the final destination in the XF which I now set to be NaN.
But if I do that, an error would show that the New columns must have the same length as old columns.
It's a simple case, it definitely has a optimal solution.
If I didn't include it inside the XF, the vehicle wouldn't move. And if I include it in XF, the error showed up