A predictive human-inspired path planning method based on the dynamic wave expansion neural network (DWENN)

Abstract

A new path planning strategy inspired by human path planning is proposed based on the dynamic wave expansion neural network (DWENN) for moving in dynamic environment. The proposed method performs in two phases. In the first phase, a coarse path is produced by using a DWENN and a cognitive map to represent the environment configuration. In the second phase, to improve the coarse path, a predictive approach is iteratively employed by combination of a locally recurrent neural network (LRNN) and a DWENN to plan a motion vector in a finite prediction horizon and executing it in a control horizon. A task is intended to evaluate the performance of the proposed method in crossing a street which includes a moving car as a dynamic obstacle. In this evaluation, different simulations with various prediction and control horizons have been performed. Our results imply that by applying a predictive method and adjusting the prediction and control horizons, DWENN can satisfactorily generate a collision-free path in dynamic environments.