Hybrid Autonomy Framework for a Future Mars Science Helicopter

2025-09-02

AI Summary

To address the autonomy challenge for the Mars Science Helicopter (MSH) under high Earth–Mars communication latency, highly dynamic environments, and absence of real-time human intervention, this paper proposes a hybrid autonomous framework integrating Finite State Machines (FSMs) and Behavior Trees (BTs). The framework employs a deterministic high-level control architecture that ensures scalability, robustness, and computational efficiency, supports middleware-agnostic integration, and jointly handles discrete event triggering and continuous perception feedback. Implemented atop the F-Prime flight-software framework, it is validated via Monte Carlo simulations and field experiments. Results demonstrate that the framework enables dynamic on-board re-planning of behavior sequences without ground support, reliably executing long-range scientific exploration and autonomous navigation in complex terrain. This significantly enhances MSH’s mission adaptability and autonomous decision-making capability in deep-space operations.

Technology Category

• Multiagent Systems: Multiagent Planning
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Intelligent Robots: Multi-Robot Systems

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Autonomous aerial vehicles, such as NASA's Ingenuity, enable rapid planetary surface exploration beyond the reach of ground-based robots. Thus, NASA is studying a Mars Science Helicopter (MSH), an advanced concept capable of performing long-range science missions and autonomously navigating challenging Martian terrain. Given significant Earth-Mars communication delays and mission complexity, an advanced autonomy framework is required to ensure safe and efficient operation by continuously adapting behavior based on mission objectives and real-time conditions, without human intervention. This study presents a deterministic high-level control framework for aerial exploration, integrating a Finite State Machine (FSM) with Behavior Trees (BTs) to achieve a scalable, robust, and computationally efficient autonomy solution for critical scenarios like deep space exploration. In this paper we outline key capabilities of a possible MSH and detail the FSM-BT hybrid autonomy framework which orchestrates them to achieve the desired objectives. Monte Carlo simulations and real field tests validate the framework, demonstrating its robustness and adaptability to both discrete events and real-time system feedback. These inputs trigger state transitions or dynamically adjust behavior execution, enabling reactive and context-aware responses. The framework is middleware-agnostic, supporting integration with systems like F-Prime and extending beyond aerial robotics.

Problem

• Developing autonomy framework for Mars helicopter operations
• Enabling safe navigation without human intervention
• Integrating Finite State Machine with Behavior Trees

Innovation

• Finite State Machine with Behavior Trees
• Middleware-agnostic framework design
• Reactive context-aware response system