We consider the problem of planning under observation and motion uncertainty for nonlinear robotics systems. Determining the optimal solution to this problem, generally formulated as a Partially Observed Markov Decision Process (POMDP), is computationally intractable. We propose a Trajectory-optimized Linear Quadratic Gaussian (T-LQG) approach that leads to quantifiably near-optimal solutions f...

A major objective of next generation reusable launch vehicle (RLV) programs includes significant improvements in vehicle safety, reliability, and operational costs. In this paper, trajectory generation on approach/landing (A&L) for RLVs using motion primitives (MPs) and neighboring optimal control (NOC) is discussed. The proposed trajectory generation approach at A&L phase is based on an MP sch...

Algorithms which guarantee to find globally optimal trajectories by covering a hypervolume of state space become infeasible in high dimensions. We have developed an algorithm which generates locally optimal trajectories without covering a hypervolume which only requires polynomial memory and time. Even though the algorithm does not guarantee a globally optimal trajectory in a deterministic mann...

Space trajectory design is often achieved through a combination of dynamical systems theory and optimal control. The union of trajectory design techniques utilizing invariant manifolds of the planar circular restricted three-body problem and the optimal control scheme Discrete Mechanics and Optimal Control (DMOC) facilitates the design of low-energy trajectories in the N -body problem. In parti...

A mission design technique that uses invariant manifold techniques together with the optimal control algorithm DMOC produces locally optimal, low ∆V trajectories. Previously, invariant manifolds of the planar circular restricted three body problem (PCR3BP) have been used to design trajectories with relatively small ∆V . Using local optimal control methods, specifically DMOC, it is possible to r...