信念空間計画の簡素化：軌道最適化LQG（T-LQG）
Belief Space Planning Simplified: Trajectory-Optimized LQG (T-LQG) (Extended Report)
信念空間計画をLQG設計の名目軌道を決定変数とすることでn次元問題に縮約し、軌道・推定器・制御器を統合設計するNLP手法を提案。既存手法より計算負荷が低いことを示す。
著者: Mohammadhussein Rafieisakhaei, Suman Chakravorty, P. R. Kumar
分類: cs.RO, math.OC
原文アブストラクト
Planning under motion and observation uncertainties requires solution of a stochastic control problem in the space of feedback policies. In this paper, we reduce the general (n^2+n)-dimensional belief space planning problem to an (n)-dimensional problem by obtaining a Linear Quadratic Gaussian (LQG) design with the best nominal performance. Then, by taking the underlying trajectory of the LQG controller as the decision variable, we pose a coupled design of trajectory, estimator, and controller design through a Non-Linear Program (NLP) that can be solved by a general NLP solver. We prove that under a first-order approximation and a careful usage of the separation principle, our approximations are valid. We give an analysis on the existing major belief space planning methods and show that our algorithm has the lowest computational burden. Finally, we extend our solution to contain general state and control constraints. Our simulation results support our design.
