  Trajectory planning is a critical process that enables autonomous systems to
safely navigate complex environments. Signal temporal logic (STL)
specifications are an effective way to encode complex temporally extended
objectives for trajectory planning in cyber-physical systems (CPS). However,
planning from these specifications using existing techniques scale
exponentially with the number of nested operators and the horizon of
specification. Additionally, performance is exacerbated at runtime due to
limited computational budgets and compounding modeling errors. Decomposing a
complex specification into smaller subtasks and incrementally planning for them
can remedy these issues. In this work, we present a way to decompose STL
requirements temporally to improve planning efficiency and performance. The key
insight in our work is to encode all specifications as a set of reachability
and invariance constraints and scheduling these constraints sequentially at
runtime. Our proposed technique outperforms the state-of-the-art trajectory
synthesis techniques for both linear and non linear dynamical systems.
