scholarly journals The adjoining cell mapping and its recursive unraveling, part II: Application to selected problems

1993 ◽  
Vol 4 (4) ◽  
pp. 309-336 ◽  
Author(s):  
R. S. Guttalu ◽  
P. J. Zufiria
Keyword(s):  
Cell Mapping ◽  
Adjoining Cell Mapping ◽  
Adjoining Cell

Optimal motion planning by reinforcement learning in autonomous mobile vehicles

Robotica ◽  
2011 ◽  
Vol 30 (2) ◽  
pp. 159-170 ◽  
Author(s):  
M. Gómez ◽  
R. V. González ◽  
T. Martínez-Marín ◽  
D. Meziat ◽  
S. Sánchez
Keyword(s):  
Reinforcement Learning ◽  
Motion Planning ◽  
Mapping Technique ◽  
Cell Mapping ◽  
Optimal Motion ◽  
State Equations ◽  
Learning Stage ◽  
Optimal Motion Planning ◽  
Mapping Techniques ◽  
Adjoining Cell Mapping

SUMMARYThe aim of this work has been the implementation and testing in real conditions of a new algorithm based on the cell-mapping techniques and reinforcement learning methods to obtain the optimal motion planning of a vehicle considering kinematics, dynamics and obstacle constraints. The algorithm is an extension of the control adjoining cell mapping technique for learning the dynamics of the vehicle instead of using its analytical state equations. It uses a transformation of cell-to-cell mapping in order to reduce the time spent during the learning stage. Real experimental results are reported to show the satisfactory performance of the algorithm.

scholarly journals Introducing MultiScale technique with CACM-RL

2017 ◽  
Vol 14 (1) ◽  
pp. 172988141769428
Author(s):  
Mariano Gómez Plaza ◽  
Tomás Arribas Navarro ◽  
Sebastián Sánchez Prieto
Keyword(s):  
State Space ◽  
Real Time ◽  
The State ◽  
Multiscale Approach ◽  
Test Cases ◽  
Resource Limited ◽  
Main Challenge ◽  
Minimum Number ◽  
Adjoining Cell Mapping ◽  
Adjoining Cell

Control Adjoining Cell Mapping and Reinforcement Learning (CACM-RL) is a promising technique used to implement controllers. However, it needs many resources so that it can be only applied to simple problems. The contribution of this work is to describe MultiScale approach in order to be used together with CACM-RL technique to overcome its limitations. The main challenge is to verify and validate its efficiency in real-time and in resource-limited systems. MultiScale approach is truly useful when different levels of resolution are needed in the state space, regardless of the number of dimensions. In this way, a set of different regions inside the state space where each region has a specific optimal policy (also different resolutions) is defined. The results described in this article show the feasibility to run MultiScale in real time and find the minimum number of policies to solve the optimal control problem in an automatic way. In the considered test cases, a significant reduction in the total number of cells used is achieved when using MultiScale.

An improved cell mapping method based on dimension-extension for fractional systems

2021 ◽  
Vol 31 (6) ◽  
pp. 063132
Author(s):  
Minjuan Yuan ◽  
Liang Wang ◽  
Yiyu Jiao ◽  
Wei Xu
Keyword(s):  
Mapping Method ◽  
Cell Mapping ◽  
Fractional Systems

Somatic cell mapping and in situ localization of the bovine uridine monophosphate synthase gene (UMPS)

1994 ◽  
Vol 5 (1) ◽  
pp. 46-47 ◽  
Author(s):  
A. M. Ryan ◽  
D. S. Gallagher ◽  
S. Schöber ◽  
B. Schwenger ◽  
J. E. Womack
Keyword(s):  
Somatic Cell ◽  
Cell Mapping ◽  
Somatic Cell Mapping ◽  
Uridine Monophosphate

Using Generalized Cell Mapping to Approximate Invariant Measures on Compact Manifolds

1997 ◽  
Vol 07 (11) ◽  
pp. 2487-2499 ◽  
Author(s):  
Rabbijah Guder ◽  
Edwin Kreuzer
Keyword(s):  
Dynamical Systems ◽  
Invariant Measures ◽  
Nonlinear Dynamical Systems ◽  
Powerful Method ◽  
Cell Mapping ◽  
Generalized Cell Mapping ◽  
Nonlinear Dynamical ◽  
Long Term Behavior ◽  
Mapping Theory

In order to predict the long term behavior of nonlinear dynamical systems the generalized cell mapping is an efficient and powerful method for numerical analysis. For this reason it is of interest to know under what circumstances dynamical quantities of the generalized cell mapping (like persistent groups, stationary densities, …) reflect the dynamics of the system (attractors, invariant measures, …). In this article we develop such connections between the generalized cell mapping theory and the theory of nonlinear dynamical systems. We prove that the generalized cell mapping is a discretization of the Frobenius–Perron operator. By applying the results obtained for the Frobenius–Perron operator to the generalized cell mapping we outline for some classes of transformations that the stationary densities of the generalized cell mapping converges to an invariant measure of the system. Furthermore, we discuss what kind of measures and attractors can be approximated by this method.

Fast cell-to-cell mapping (path integration) for nonlinear white noise and Poisson driven systems

1995 ◽  
Vol 17 (3) ◽  
pp. 151-165 ◽  
Author(s):  
H.U. Köylüolu ◽  
S.R.K. Nielsen ◽  
A.Ş. Çakmak
Keyword(s):  
White Noise ◽  
Path Integration ◽  
Cell Mapping ◽  
Driven Systems
Sign in / Sign up

Export Citation Format

Share Document