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Trajectory optimization for lunar soft landing with complex constraints

机译:复杂约束月球软着陆的航迹优化

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摘要

A unified trajectory optimization framework with initialization strategies is proposed in this paper for lunar soft landing for various missions with specific requirements. Two main missions of interest are Apollo-like Landing from low lunar orbit and Vertical Takeoff Vertical Landing (a promising mobility method) on the lunar surface. The trajectory optimization is characterized by difficulties arising from discontinuous thrust, multi-phase connections, jump of attitude angle, and obstacles avoidance. Here R-function is applied to deal with the discontinuities of thrust, checkpoint constraints are introduced to connect multiple landing phases, attitude angular rate is designed to get rid of radical changes, and safeguards are imposed to avoid collision with obstacles. The resulting dynamic problems are generally with complex constraints. The unified framework based on Gauss Pseudospectral Method (GPM) and Nonlinear Programming (NLP) solver are designed to solve the problems efficiently. Advanced initialization strategies are developed to enhance both the convergence and computation efficiency. Numerical results demonstrate the adaptability of the framework for various landing missions, and the performance of successful solution of difficult dynamic problems.
机译:本文提出了一种具有初始化策略的统一轨迹优化框架,用于各种有特殊要求的任务的月球软着陆。感兴趣的两个主要任务是从低月球轨道进行的类似阿波罗的着陆和月球表面的垂直起飞垂直着陆(一种有希望的机动性方法)。轨迹优化的特点是,由于不连续的推力,多相连接,姿态角的跳跃和避障而产生的困难。在这里,应用R函数来处理推力的不连续性,引入检查点约束以连接多个着陆阶段,设计姿态角速率以消除根本性的变化,并采取了避免与障碍物碰撞的保护措施。产生的动态问题通常具有复杂的约束。设计了基于高斯伪谱方法(GPM)和非线性规划(NLP)求解器的统一框架,以有效地解决问题。开发了高级初始化策略以增强收敛性和计算效率。数值结果证明了该框架对各种着陆任务的适应性,以及成功解决难题的动态性能。

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