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Expanding Kinodynamic Optimization Solutions with Recurrent Neural Networks and Path-tracking Control

This paper explores a methodology for training recurrent neural networks in replicating path planning solutions from optimization problems. Training data is generated from a kinodynamic rapidly-exploring random tree, from which a recurrent neural network is trained upon to produce the state path through fixed time-step execution. Path-tracking controllers are formulated to follow the path generated by the network alongside the use of local potential functions to mitigate minor constraint violations. The control signal from such a controller should mimic that of the optimized solution. Preliminary results for a 2D dynamics problem with obstacle constraints showcase the ability of this approach to achieve the desired controller execution and resulting state path. We also show that better network training and greater amounts of data lead to an increase in the overall performance.