  Optimizing high-level mission planning constraints is traditionally solved in
exponential time and requires to split the problem into several ones, making
the connections between them a convoluted task. This paper aims at generalizing
recent works on the convexification of Signal Temporal Logic (STL) constraints
converting them into linear approximations. Graphs are employed to build
general linguistic semantics based on key words (such as Not, And, Or,
Eventually, Always), and super-operators (e.g., Until, Implies, If and Only If)
based on already defined ones. Numerical validations demonstrate the
performance of the proposed approach on two practical use-cases of satellite
optimal guidance using a modified Successive Convexification scheme.
