Chance-Constrained Sequential Convex Programming for Robust Trajectory Optimization
Chance-Constrained Sequential Convex Programming for Robust Trajectory Optimization
复制标题
DOI：
10.23919/ecc51009.2020.9143595
复制
发表时间：
2020-05
期刊：
影响因子：
--
通讯作者：
T. Lew;Riccardo Bonalli;M. Pavone
中科院分区：
文献类型：
--
作者：
T. Lew;Riccardo Bonalli;M. Pavone
规划非线性动力学系统的安全轨迹模型的不确定性和干扰是具有挑战性的。在这项工作中，我们提出了一种新的方法来解决机会约束的轨迹规划问题与非凸约束，从而避免障碍物的机会约束重新制定使用有符号的距离函数。我们提出了一种新的序列凸规划算法，并证明了在离散时间问题的提法，它是保证收敛到一个解决方案满足一阶最优性条件。我们证明了一个不确定的6自由度航天器系统的方法，并表明解决方案满足一组给定的机会约束。
Planning safe trajectories for nonlinear dynamical systems subject to model uncertainty and disturbances is challenging. In this work, we present a novel approach to tackle chance-constrained trajectory planning problems with nonconvex constraints, whereby obstacle avoidance chance constraints are reformulated using the signed distance function. We propose a novel sequential convex programming algorithm and prove that under a discrete time problem formulation, it is guaranteed to converge to a solution satisfying first-order optimality conditions. We demonstrate the approach on an uncertain 6 degrees of freedom spacecraft system and show that the solutions satisfy a given set of chance constraints.