  In this research, we aim to answer the question: How to combine Closed-Loop
State and Input Sensitivity-based with Observability-aware trajectory planning?
These possibly opposite optimization objectives can be used to improve
trajectory control tracking and, at the same time, estimation performance. Our
proposed novel Control & Observability-aware Planning (COP) framework is the
first that uses these possibly opposing objectives in a Single-Objective
Optimization Problem (SOOP) based on the Augmented Weighted Tchebycheff method
to perform the balancing of them and generation of B\'ezier curve-based
trajectories. Statistically relevant simulations for a 3D quadrotor unmanned
aerial vehicle (UAV) case study produce results that support our claims and
show the negative correlation between both objectives. We were able to reduce
the positional mean integral error norm as well as the estimation uncertainty
with the same trajectory to comparable levels of the trajectories optimized
with individual objectives.
