Differential dynamic programming for optimal estimation

This paper studies an optimization-based approach for solving optimal estimation and optimal control problems through a unified computational formulation. The goal is to perform trajectory estimation over extended past horizons and model-predictive control over future horizons by enforcing the same dynamics, control, and sensing constraints in both problems, and thus solving both problems with identical computational tools. Through such systematic estimation-control formulation we aim to improve the performance of autonomous systems such as agile robotic vehicles. This work focuses on sequential sweep trajectory optimization methods, and more specifically extends the method known as differential dynamic programming to the parameter-dependent setting in order to enable the solutions to general estimation and control problems.

Authors

• Marin Kobilarov
• Duy-Nguyen Ta
• Frank Dellaert

Keywords

• Estimation
• Trajectory
• Standards
• Optimal control
• Optimization
• Dynamic programming
• Convergence
• Differentiation Program
• Optimal Estimation
• Differential Dynamic Programming
• Optimization Method
• Control Problem
• Estimation Problem
• Autonomous Vehicles
• Optimal Control Problem
• Trajectory Optimization
• Future Horizon
• Dynamical
• Value Function
• Types Of Methods
• Rigid Body
• Aerial Vehicles
• Nonlinear Programming
• Newton Method
• Interior Point Method
• Extended Kalman Filter
• Autonomous Underwater Vehicles
• Simultaneous Localization And Mapping
• Forward Sweep
• Underwater Vehicles
• Linear Quadratic Regulator
• Sweep Method
• Terminal Cost
• Smooth Problems
• Gauss-Newton Method
• Small Eigenvalues