  Current motion planning approaches for autonomous mobile robots often assume
that the low level controller of the system is able to track the planned motion
with very high accuracy. In practice, however, tracking error can be affected
by many factors, and could lead to potential collisions when the robot must
traverse a cluttered environment. To address this problem, this paper proposes
a novel receding-horizon motion planning approach based on Model Predictive
Path Integral (MPPI) control theory -- a flexible sampling-based control
technique that requires minimal assumptions on vehicle dynamics and cost
functions. This flexibility is leveraged to propose a motion planning framework
that also considers a data-informed risk function. Using the MPPI algorithm as
a motion planner also reduces the number of samples required by the algorithm,
relaxing the hardware requirements for implementation. The proposed approach is
validated through trajectory generation for a quadrotor unmanned aerial vehicle
(UAV), where fast motion increases trajectory tracking error and can lead to
collisions with nearby obstacles. Simulations and hardware experiments
demonstrate that the MPPI motion planner proactively adapts to the obstacles
that the UAV must negotiate, slowing down when near obstacles and moving
quickly when away from obstacles, resulting in a complete reduction of
collisions while still producing lively motion.
