Quadrotors are agile flying robots that are challenging to control. Considering the full dynamics of quadrotors during motion planning is crucial to achieving good solution quality and small tracking errors during flight. Optimization-based methods scale well with high-dimensional state spaces and can handle dynamic constraints directly, therefore they are often used in these scenarios. The resulting optimization problem is notoriously difficult to solve due to its nonconvex constraints. In this work, we present an analysis of four solvers for nonlinear trajectory optimization (KOMO, direct collocation with SCvx, direct collocation with CasADi, Crocoddyl) and evaluate their performance in scenarios where the solvers are tasked to find minimum-effort solutions to geometrically complex problems and problems requiring highly dynamic solutions. Benchmarking these methods helps to determine the best algorithm structures for these kinds of problems.

翻译：四旋翼飞行器是敏捷的飞行机器人，其控制具有挑战性。在运动规划中考虑四旋翼的完整动力学对于实现良好的解质量和飞行过程中较小的跟踪误差至关重要。基于优化的方法在高维状态空间中具有良好的扩展性，并且能够直接处理动力学约束，因此常用于此类场景。然而，由此产生的优化问题因其非凸约束而难以求解。在本研究中，我们分析了四种非线性轨迹优化求解器（KOMO、基于SCvx的直接配点法、基于CasADi的直接配点法、Crocoddyl），并评估了它们在求解需要最小化控制代价的几何复杂问题和高动态问题时的性能。对这些方法进行基准测试有助于确定解决此类问题的最佳算法结构。