Autonomous Navigation in Complex Environments
This work addresses navigation for rescue robots in complex, unknown settings, but it is incremental as it applies existing methods to a new simulated scenario.
The paper tackles autonomous robot navigation in simulated subterranean rescue environments by using CNN-DNN fusion and imitation learning with LiDAR and camera data, achieving robustness tested via Monte-Carlo methods.
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.