3. Creating Constraints

• 3. Creating Constraints
• • Creating Constraint Sets

Creating Constraint Sets

The easiest way to set up the constraints for your problem is through the ConstraintList. This structure simply holds a vector of all the constraints in the trajectory optimization problem. The easiest way to start is to create an empty ConstraintList:

cons = ConstraintList(n,m,N)

Adding Constraints

You can add any constraint (the list of currently implemented constraints is given in the following section) to the constraint set using the add_constraint! method. For example, if we want to add control limits and an final goal constraint to our problem, we do this by creating a ConstraintList and subsequently adding the constraints:

# Dimensions of our problem
n,m,N = 4,1,51    # 51 knot points

# Create our list of constraints
cons = ConstraintList(n,m,N)

# Create the goal constraint
xf = [0,π,0,0]
goalcon = GoalConstraint(xf)
add_constraint!(cons, goalcon, N)  # add to the last time step

# Create control limits
ubnd = 3
bnd = BoundConstraint(n,m, u_min=-ubnd, u_max=ubnd)
add_constraint!(cons, bnd, 1:N-1)  # add to all but the last time step

Defined Constraints

The following constraints are currently defined. See their individual docstrings on details on how to construct them, since constraint constructors are, in general, unique to the constraint.

• GoalConstraint
• LinearConstraint
• CircleConstraint
• SphereConstraint
• CollisionConstraint
• NormConstraint
• BoundConstraint
• IndexedConstraint

Working with ConstraintList

The ConstraintList type is useful type that provides some convenient methods for manipulating and working with the constraints in your trajectory optimization problem.

It supports many of the operations that work on an AbstractVector, such as length, indexing (e.g. cons[1] or cons[end]), and iteration (e.g. [con for con in cons]).

The time step indices can be retrieved via constraintindices, e.g. constraintindices(cons, 1), which returns the indices for the first constraint. You can also iterate over constraints and their time indices simultaneously using zip(::ConstraintList), e.g. [(length(con), length(inds)) for (inds,con) in zip(cons)].

The num_constraints(::ConstraintList) returns a vector of length N with total number of constraint values per time step. Note that since each constraint is vector-valued, this is different than length(::ConstraintList). The total number of dual variables can be calculated using sum(num_constraints(cons)).