Autonomous Navigation with Online Replanning and Recovery Behaviors for Wheeled-Legged Robots Using Behavior Trees

Abstract

Performing autonomous navigation in cluttered and unstructured terrains still remains a challenging task for legged and wheeled mobile robots. To accomplish such a task, online planners shall incorporate new terrain information perceived while the robot is moving within its environment. While hybrid mobility robots offer high flexibility in traversing challenging terrains by leveraging the advantages of both wheeled and legged locomotion, the effective hybrid planning of the mobility actions that transparently combine both modes of locomotion has not been extensively explored. In this work, we present a hierarchical online hybrid primitive-based planner for autonomous navigation with wheeled-legged robots. The framework is handled by a Behavior Tree (BT) and it takes into account recovery methods to deal with possible failures during the execution of the navigation/mobility plan. The framework was evaluated in multiple irregular and heavily cluttered simulated environments randomly generated and in real-world trials, using the CENTAURO robot platform. With these experiments, we demonstrated autonomous capabilities without any human intervention, even in case of collision or planner failures.