Self-organizing multi-resolution grid for motion planning and control
T. Fomin, T. Rozgonyi, Cs. Szepesvári and A. Lõrincz
International Journal of Neural Systems
7,
757--776
(1996)
Abstract
A fully self-organizing neural network approach to low-dimensional
control problems is described. We consider the problem of learning
to control an object and solving the path planning problem at the
same time. Control is based on the path planning model that follows
the gradient of the stationary solution of a diffusion process working
in the state space. Previous works are extended by introducing a
self-organizing multigrid-like discretizing structure to represent the
external world. Diffusion is simulated within a recurrent neural network
built on this multigrid system. The novelty of the approach is that the
diffusion on the multigrid is fast. Moreover, the diffusion process on
the multigrid fits well the requirements of the path planning: it
accelerates the diffusion in large free space regions while still keeps
the resolution in small bottleneck-like labyrinths along the path.
Control is achieved in the usual way: associative learning identifies the
inverse dynamics of the system in a direct fashion. To this end there are
introduced interneurons between neighbouring discretizing units that
detect the strength of the steady-state diffusion and forward control
commands to the control neurons via modifiable connections. This
architecture forms the Multigrid Position-and-Direction-to-Action (MPDA)
map. The architecture integrates reactive path planning and continuous
motion control. It is also shown that the scheme leads to population
coding for the actual command vector.
