  Model Predictive Path Integral (MPPI) control has proven to be a powerful
tool for the control of uncertain systems (such as systems subject to
disturbances and systems with unmodeled dynamics). One important limitation of
the baseline MPPI algorithm is that it does not utilize simulated trajectories
to their fullest extent. For one, it assumes that the average of all
trajectories weighted by their performance index will be a safe trajectory. In
this paper, multiple examples are shown where the previous assumption does not
hold, and a trajectory clustering technique is presented that reduces the
chances of the weighted average crossing in an unsafe region. Secondly, MPPI
does not account for dynamic obstacles, so the authors put forward a novel cost
function that accounts for dynamic obstacles without adding significant
computation time to the overall algorithm. The novel contributions proposed in
this paper were evaluated with extensive simulations to demonstrate
improvements upon the state-of-the-art MPPI techniques.
