Hybrid Autonomy Framework for a Future Mars Science Helicopter

Autonomous aerial vehicles, such as NASA’s Ingenuity, enable rapid planetary surface exploration beyond thereach of ground-based robots. Thus, NASA is working on aMars Science Helicopter (MSH), an advanced concept capable ofperforming long-range science missions and autonomously navigating challenging Martian terrain. Given significant EarthMars communication delays and mission complexity, an advanced autonomy framework is required to ensure safe andefficient operation by continuously adapting behavior basedon mission objectives and real-time conditions, without humanintervention. This study presents a deterministic high-levelcontrol framework for aerial exploration, integrating a FiniteState Machine (FSM) with Behavior Trees (BTs) to achievea scalable, robust, and computationally efficient autonomysolution for critical scenarios like deep space exploration. In thispaper we outline key capabilities of a possible MSH and detailthe FSM-BT hybrid autonomy framework which orchestratesthem to achieve the desired objectives. Monte Carlo simulationsand real field tests validate the framework, demonstrating itsrobustness and adaptability to both discrete events and realtime system feedback. These inputs trigger state transitionsor dynamically adjust behavior execution, enabling reactiveand context-aware responses. The framework is middlewareagnostic, supporting integration with systems like F-Prime andextending beyond aerial robotics.