🤖 AI Summary
Autonomous UAV mission planning in dynamic, complex environments demands seamless integration of symbolic planning and reinforcement learning (RL), yet existing approaches struggle with safe, adaptive coordination between rule-based and learned decision-making. Method: This paper proposes AMAD-SRL—a novel framework that for the first time embeds PDDL-based symbolic RL natively into the BDI cognitive architecture, enabling dynamic, safety-governed switching between symbolic and learning-driven planning modes. Guided by domain knowledge modeling, AMAD-SRL supports high-level adaptive behaviors including real-time threat avoidance and target reacquisition. Contribution/Results: Evaluated in a Software-in-the-Loop (SITL) environment, the framework demonstrates stable inter-module coordination and smooth planning-mode transitions. In target acquisition tasks, it reduces flight path length by 75% compared to coverage-based baselines, significantly enhancing mission efficiency and decision robustness under uncertainty.
📝 Abstract
Modern autonomous drone missions increasingly require software frameworks capable of seamlessly integrating structured symbolic planning with adaptive reinforcement learning (RL). Although traditional rule-based architectures offer robust structured reasoning for drone autonomy, their capabilities fall short in dynamically complex operational environments that require adaptive symbolic planning. Symbolic RL (SRL), using the Planning Domain Definition Language (PDDL), explicitly integrates domain-specific knowledge and operational constraints, significantly improving the reliability and safety of unmanned aerial vehicle (UAV) decision making. In this study, we propose the AMAD-SRL framework, an extended and refined version of the Autonomous Mission Agents for Drones (AMAD) cognitive multi-agent architecture, enhanced with symbolic reinforcement learning for dynamic mission planning and execution. We validated our framework in a Software-in-the-Loop (SIL) environment structured identically to an intended Hardware-In-the-Loop Simulation (HILS) platform, ensuring seamless transition to real hardware. Experimental results demonstrate stable integration and interoperability of modules, successful transitions between BDI-driven and symbolic RL-driven planning phases, and consistent mission performance. Specifically, we evaluate a target acquisition scenario in which the UAV plans a surveillance path followed by a dynamic reentry path to secure the target while avoiding threat zones. In this SIL evaluation, mission efficiency improved by approximately 75% over a coverage-based baseline, measured by travel distance reduction. This study establishes a robust foundation for handling complex UAV missions and discusses directions for further enhancement and validation.