Traversability Cost Identification of Dynamic Environments Using Recurrent High Order Neural Networks for Robot Navigation
In this paper, it is presented a neural network methodology for learning traversability cost maps to aid autonomous robotic navigation. This proposal is based on the control theory idea for dynamical system identification i.e. we solve the problem of learning and recognizing the pattern which describes the best the behavior of the cost function that represents the environment to obtain traversability cost maps as if we are identifying a dynamical system that is the rough terrain where the robot navigates. Recurrent High Order Neural Networks (RHONN) trained with Extended Kalman Filter (EKF) are used to identify rough terrain traversability costs, and besides the good results in the identification tasks, we get the advantages of using a robust machine learning method such as RHONNs. Our proposal gives the robot the capability to generalize the knowledge learned in previous navigation episodes when it is navigating on similar (but not necessarily equal) environments, so the robot can re-use learned knowledge, generalize it and also it can recognize hidden states. Experimental results show that our proposed approach can identify and learn very complex cost maps, we prove it with artificially generated maps as well as satellite maps of real land.
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