  The success of the model predictive path integral control (MPPI) approach
depends on the appropriate selection of the input distribution used for
sampling. However, it can be challenging to select inputs that satisfy output
constraints in dynamic environments. The main contribution of this paper is to
propose an output-sampling-based MPPI (o-MPPI), which improves the ability of
samples to satisfy output constraints and thereby increases MPPI efficiency.
Comparative simulations and experiments of dynamic autonomous driving of bots
around a track are provided to show that the proposed o-MPPI is more efficient
and requires substantially (20-times) less number of rollouts and (4-times)
smaller prediction horizon when compared with the standard MPPI for similar
success rates. The supporting video for the paper can be found at
https://youtu.be/snhlZj3l5CE.
