Flexible path planning for real-time applications using A*-method and neural RBF-networks

We developed a generally applicable concept for flexible path planning and representation in high-dimensional configuration spaces. Therefore, an AI-algorithm for fast preprocessing and a neural network were combined. Specifically, the standard A*-method was developed into an advanced A*-method (AA*-method) by creating an additional class of FREE-cells to enlarge the computed surroundings of the detected optimal path, and by constituting expansion matrices to enable flexible modeling of different cell extents and configuration spaces. Furthermore, a neural RBF-network was modified by adding an activity peak generating neuron guaranteeing updates in real-time (less than 1 ms). The output of the AA*-method, a set of classified cells, was used to train the modified RBF-network. The capabilities of this novel hybrid path planning system are demonstrated for various complex 3D- and 6D- path planning tasks