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.

翻译：针对自主移动机器人，环境与系统模型的不确定性可能导致运动规划管线的失效，进而引发潜在碰撞风险。为实现高水平的鲁棒自主性，机器人需具备主动预测此类失效并实施恢复的能力。为此，我们提出一种基于高斯过程的预测模型，用于主动检测未来运动规划失效的风险。当该风险超过设定阈值时，将触发恢复行为——该行为利用同一高斯过程模型寻找安全状态，使机器人能够继续朝向目标前进。所提方法仅需通过仿真训练，即可泛化至不同机器人平台的真实环境。仿真与物理实验表明，本框架既能预测规划器的失效情况，又能将机器人恢复至规划成功概率较高的状态，同时保持敏捷运动能力。