Path Planning Algorithm, Based on User Defined Maximal Localization Error

A model based path planning algorithm will be presented in this paper. The whole model, as 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 [12] . The 2nd part begin with the calculation of the environment’s 2D mathematical model. The calculation is realized through the thresholding of the global potential map. Furthermore, a landmark arrangement will be defined on this model. The Artificial 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 AEF contains the localization errors corresponding to each “x, y” position of the work space’s free space. In respect of the user-defined maximal localization error (max), some navigation paths (NP) can be generated. These paths serve as the base for the calculation of the possible routes in form of directed and weighted graph-map. The route with minimal complexity between the start and docking positions on this graph-map will be selected. Each point of the mobile robot’s exact trajectory must fit within 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.