  Most commercially available fixed-wing aerial vehicles (FWV) can carry only
small, lightweight computing hardware such as Jetson TX2 onboard. Solving
non-linear trajectory optimization on these computing resources is
computationally challenging even while considering only the kinematic motion
model. Most importantly, the computation time increases sharply as the
environment becomes more cluttered. In this paper, we take a step towards
overcoming this bottleneck and propose a trajectory optimizer that achieves
online performance on both conventional laptops/desktops and Jetson TX2 in a
typical urban environment setting. Our optimizer builds on the novel insight
that the seemingly non-linear trajectory optimization problem for FWV has an
implicit multi-convex structure. Our optimizer exploits these computational
structures by bringing together diverse concepts from Alternating Minimization,
Bregman iteration, and Alternating Direction Method of Multipliers. We show
that our optimizer outperforms the state-of-the-art implementation of
sequential quadratic programming approach in optimal control solver ACADO in
computation time and solution quality measured in terms of control and goal
reaching cost.
