Adaptive look-ahead for robotic navigation in unknown environments

Receding horizon control strategies have proven effective in many control and robotic applications. These methods simulate the state a certain time horizon into the future to choose the optimal trajectory. However, in many cases, such as in mobile robot navigation, the selection of an appropriate time horizon is important as too long of a time horizon can amplify the detrimental effects caused by environmental uncertainties in the prediction of the future state while too of a short horizon will lead to reduced performance. In this paper we strike a balance between these two conflicting objectives by first introducing a receding horizon method for navigation founded on schema-based behaviors. We then suggest a method of adapting the time horizon by minimizing a cost function which balances the performance of the underlying control problem (which prefers longer horizons) with the performance of our state prediction (which prefers shorter time horizons). We illustrate the operation with an example which shows the usefulness of our navigation scheme with an adaptive time horizon.