Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Rafieisakhaei, Mohammadhussein"'
Autor:
Barazandeh, Babak, Rafieisakhaei, Mohammadhussein, Kim, Sunwook, Zhenyu, Kong, Nussbaum, Maury A.
Classification methods based on sparse estimation have drawn much attention recently, due to their effectiveness in processing high-dimensional data such as images. In this paper, a method to improve the performance of a sparse representation classif
Externí odkaz:
http://arxiv.org/abs/1810.09447
Publikováno v:
2017 IEEE 56th Annual Conference on Decision and Control (CDC)
Optimizing measures of the observability Gramian as a surrogate for the estimation performance may provide irrelevant or misleading trajectories for planning under observation uncertainty.
Comment: 6 pages, 9 figures. CDC 2017
Comment: 6 pages, 9 figures. CDC 2017
Externí odkaz:
http://arxiv.org/abs/1801.09877
This paper studies the partially observed stochastic optimal control problem for systems with state dynamics governed by partial differential equations (PDEs) that leads to an extremely large problem. First, an open-loop deterministic trajectory opti
Externí odkaz:
http://arxiv.org/abs/1711.01167
This paper studies the partially observed stochastic optimal control problem for systems with state dynamics governed by Partial Differential Equations (PDEs) that leads to an extremely large problem. First, an open-loop deterministic trajectory opti
Externí odkaz:
http://arxiv.org/abs/1707.03092
This paper studies the stochastic optimal control problem for systems with unknown dynamics. First, an open-loop deterministic trajectory optimization problem is solved without knowing the explicit form of the dynamical system. Next, a Linear Quadrat
Externí odkaz:
http://arxiv.org/abs/1705.09761
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 computatio
Externí odkaz:
http://arxiv.org/abs/1705.09415
We consider nonlinear stochastic systems that arise in path planning and control of mobile robots. As is typical of almost all nonlinear stochastic systems, the optimally solving problem is intractable. We provide a design approach which yields a tra
Externí odkaz:
http://arxiv.org/abs/1705.08566
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 prob
Externí odkaz:
http://arxiv.org/abs/1608.03013
Simultaneous Localization and Planning (SLAP) under process and measurement uncertainties is a challenge. It involves solving a stochastic control problem modeled as a Partially Observed Markov Decision Process (POMDP) in a general framework. For a c
Externí odkaz:
http://arxiv.org/abs/1605.01776
Planning under process and measurement uncertainties is a challenging problem. In its most general form it can be modeled as a Partially Observed Markov Decision Process (POMDP) problem. However POMDPs are generally difficult to solve when the underl
Externí odkaz:
http://arxiv.org/abs/1511.05186