A model-based path planning algorithm will be presented in this
paper. The whole model, like the algorithm, is divided into 2 parts. The
1st part begins with a map-building process over an unknown
environment, which is based on the construction of the so-called
Potential Field (PF) of the environment. In this
part, the above mentioned PF will be created by three autonomous robots,
equipped with US sensors, which will cooperate and update the
global potential map on the remote host.
The 2nd part begins with the calculation of the
environment´s 2D mathematical model. The calculation is realized through the
ythresholding of the global potential map.
Furthermore, a landmark arrangement will be defined on this model. The
yArtificial Error Field (AEF), which covers the
entire workspace, will be calculated and the result will depend on the
sensory system of the mobile robot/robots and on the landmark arrangement.
Actually, the three-dimensional yAEF contains the
ylocalization errors corresponding to each
`x, y´ position of the work space´s free space.
In respect of the yuser-defined maximal
localization error (ϵ\max), some
ynavigation paths (NP) can be generated. These
paths serve as the base for the calculation of the possible routes in form
of directed and yweighted graph-map. The route
with yminimal complexity between the start
and the ydocking
positions on this graph-map will be selected. Each point of the mobile
robot´s exact trajectory must fit in the selected navigation path (NP).
This maintains the allowed position error below the defined limit. The shape
of the trajectory is calculated by the use of cubic B-splines