论文标题
TRON：具有非平滑成本功能的快速求解器进行轨迹优化的求解器
TRON: A Fast Solver for Trajectory Optimization with Non-Smooth Cost Functions
论文作者
论文摘要
轨迹优化是控制和计划复杂，不足的机器人的重要工具，并且在现实世界的机器人任务中显示出令人印象深刻的结果。但是，在要优化的成本函数的应用程序中，现代轨迹优化方法的收敛性非常缓慢。在这项工作中，我们提出了Tron，这是一种迭代求解器，可用于在具有光滑组件组成的非平滑成本函数的应用中进行有效的轨迹优化。 TRON通过利用目标结构来适应成本函数来实现这一目标，从而产生一系列可以有效优化的目标。事实证明，当动力学是线性时，TRON可确保收敛到非平滑凸成本函数的全局最佳，以及当动力学是非线性时的固定点。从经验上讲，与其他轨迹优化方法相比，TRON在一系列模拟任务上的收敛速度和最终成本较低，包括移动机器人的无碰撞运动计划，手术针头的稀疏最佳控制以及卫星集合问题。
Trajectory optimization is an important tool for control and planning of complex, underactuated robots, and has shown impressive results in real world robotic tasks. However, in applications where the cost function to be optimized is non-smooth, modern trajectory optimization methods have extremely slow convergence. In this work, we present TRON, an iterative solver that can be used for efficient trajectory optimization in applications with non-smooth cost functions that are composed of smooth components. TRON achieves this by exploiting the structure of the objective to adaptively smooth the cost function, resulting in a sequence of objectives that can be efficiently optimized. TRON is provably guaranteed to converge to the global optimum of the non-smooth convex cost function when the dynamics are linear, and to a stationary point when the dynamics are nonlinear. Empirically, we show that TRON has faster convergence and lower final costs when compared to other trajectory optimization methods on a range of simulated tasks including collision-free motion planning for a mobile robot, sparse optimal control for surgical needle, and a satellite rendezvous problem.
