  This paper contributes a method to design a novel navigation planner
exploiting a learning-based collision prediction network. The neural network is
tasked to predict the collision cost of each action sequence in a predefined
motion primitives library in the robot's velocity-steering angle space, given
only the current depth image and the estimated linear and angular velocities of
the robot. Furthermore, we account for the uncertainty of the robot's partial
state by utilizing the Unscented Transform and the uncertainty of the neural
network model by using Monte Carlo dropout. The uncertainty-aware collision
cost is then combined with the goal direction given by a global planner in
order to determine the best action sequence to execute in a receding horizon
manner. To demonstrate the method, we develop a resilient small flying robot
integrating lightweight sensing and computing resources. A set of simulation
and experimental studies, including a field deployment, in both cluttered and
perceptually-challenging environments is conducted to evaluate the quality of
the prediction network and the performance of the proposed planner.
