  The key problem for autonomous robots is how to navigate through complex,
obstacle filled environments. The navigation problem is broken up into
generating a reference path to the goal and then using a local planner to track
the reference path and avoid obstacles by generating velocity and steering
references for a control system to execute.
  This paper presents a novel local planner architecture for path following and
obstacle avoidance by proposing a hybrid system that uses a classic path
following algorithm in parallel with a neural network. Our solution, called the
modification architecture, uses a path following algorithm to follow a
reference path and a neural network to modify the references generated by the
path follower in order to prevent collisions. The neural network is trained
using reinforcement learning to deviate from the reference trajectory just
enough to avoid crashing. We present our local planner in a hierarchical
planning framework, nested between a global reference planner and a low-level
control system. The system is evaluated in the context of F1/10th scale
autonomous racing in random forests and on a race track. The results
demonstrate that our solution is able to follow a reference trajectory while
avoiding obstacles using only 10 sparse laser range finder readings. Our hybrid
system overcomes previous limitations by not requiring an obstacle map, human
demonstrations, or an instrumented training setup.
