On Robust Context-Aware Navigation for Autonomous Ground Vehicles

We propose a context-aware navigation framework designed to support the navigation of autonomous ground vehicles, including articulated ones. The proposed framework employs a behavior tree with novel nodes to manage the navigation tasks: planner and controller selections, path planning, path following, and recovery. It incorporates a weather detection system and configurable global path planning and controller strategy selectors implemented as behavior tree action nodes. These components are integrated into a sub-tree that supervises and manages available options and parameters for global planners and control strategies by evaluating map and real-time sensor data. The proposed approach offers three key benefits: overcoming the limitations of single planner strategies in challenging scenarios; ensuring efficient path planning by balancing between optimization and computational effort; and achieving smoother navigation by reducing path curvature and improving drivability. The performance of the proposed framework is analyzed empirically, and compared against state of the art navigation systems with single path planning strategies.