A GP-based Robust Motion Planning Framework for Agile Autonomous Robot Navigation and Recovery in Unknown Environments
This work addresses robust autonomy for mobile robots in unknown environments, representing an incremental improvement by applying GP models to a known bottleneck in motion planning.
The paper tackles the problem of motion planning failures in autonomous robots due to environmental uncertainties by proposing a Gaussian Process-based framework for proactive failure detection and recovery, achieving agile motion with demonstrated success in simulations and physical experiments.
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.