Tom Vodopivec and Branko Šter
Mobile robot, motion planning, topological modelling, recurrent neural networks
In this paper, we use a special architecture of recurrent neural networks (RNNs) to enhance a topological approach to mobile robot navigation using RNNs. This architecture selectively latches presumably relevant input information and ignores presumably irrelevant input information. Simple types of reactive behavior are supplemented with random decisions to switch between them at decision points. The RNN is trained on a sequence of sensory contents and actions. This well-known approach is applicable to multi-step prediction of sensory information and the travelled distances between decision points, given a sequence of decisions at decision points. Thus, the optimal path to a specified goal can be sought. A problem of this approach is that due to inherent inability to design a perfect reactive behaviour, unwanted situations may appear, such as redundant decision points, unreliable switching among behaviours. We demonstrate that the applied type of RNN lowers the impact of faulty decision points and thus improves the prediction.
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