Geometrically Constrained Trajectory Optimization for Multicopters

TLDR

The authors propose an optimization‑based framework for multicopter trajectory planning that incorporates geometrical configuration constraints and user‑defined dynamic constraints. The framework employs a novel trajectory representation derived from optimality conditions for unconstrained control‑effort minimization, linear‑complexity spatial–temporal deformation, smooth maps to remove geometrical constraints, and a decoupled dense‑constraint evaluation via backward differentiation of a flatness map. Results show that the framework converts constrained multicopter planning into an unconstrained optimization that is solved reliably and efficiently, achieving high‑quality solutions across diverse flight tasks while outperforming specialized methods by orders of magnitude in computation speed.

Abstract

In this article, we present an optimization-based framework for multicopter trajectory planning subject to geometrical configuration constraints and user-defined dynamic constraints. The basis of the framework is a novel trajectory representation built upon our novel optimality conditions for unconstrained control effort minimization. We design linear-complexity operations on this representation to conduct spatial–temporal deformation under various planning requirements. Smooth maps are utilized to exactly eliminate geometrical constraints in a lightweight fashion. A variety of state-input constraints are supported by the decoupling of dense constraint evaluation from sparse parameterization and the backward differentiation of flatness map. As a result, this framework transforms a generally constrained multicopter planning problem into an unconstrained optimization that can be solved reliably and efficiently. Our framework bridges the gaps among solution quality, planning efficiency, and constraint fidelity for a multicopter with limited resources and maneuvering capability. Its generality and robustness are both demonstrated by applications to different flight tasks. Extensive simulations and benchmarks are also conducted to show its capability of generating high-quality solutions while retaining the computation speed against other specialized methods by orders of magnitude.