A GP-based Robust Motion Planning Framework for Agile Autonomous Robot
  Navigation and Recovery in Unknown Environments
Nicholas Mohammad Jacob Higgins Nicola Bezzo
For autonomous mobile robots, uncertainties in the environment and system
model can lead to failure in the motion planning pipeline, resulting in
potential collisions. In order to achieve a high level of robust autonomy,
these robots should be able to proactively predict and recover from such
failures. To this end, we propose a Gaussian Process (GP) based model for
proactively detecting the risk of future motion planning failure. When this
risk exceeds a certain threshold, a recovery behavior is triggered that
leverages the same GP model to find a safe state from which the robot may
continue towards the goal. The proposed approach is trained in simulation only
and can generalize to real world environments on different robotic platforms.
Simulations and physical experiments demonstrate that our framework is capable
of both predicting planner failures and recovering the robot to states where
planner success is likely, all while producing agile motion.