Autonomous Navigation in Complex Environments
Andrew Gerstenslager Jomol Lewis Liam McKenna Poorva Patel
This paper explores the application of CNN-DNN network fusion to construct a
robot navigation controller within a simulated environment. The simulated
environment is constructed to model a subterranean rescue situation, such that
an autonomous agent is tasked with finding a goal within an unknown cavernous
system. Imitation learning is used to train the control algorithm to use LiDAR
and camera data to navigate the space and find the goal. The trained model is
then tested for robustness using Monte-Carlo.