Application of Unmanned Aerial Vehicles (UAVs) in search and rescue, emergency management, and law enforcement has gained traction with the advent of low-cost platforms and sensor payloads. The emergence of hybrid neural and symbolic AI approaches for complex reasoning is expected to further push the boundaries of these applications with decreasing levels of human intervention. However, current UAV simulation environments lack semantic context suited to this hybrid approach. To address this gap, HAMERITT (Hybrid Ai Mission Environment for RapId Training and Testing) provides a simulation-based autonomy software framework that supports the training, testing and assurance of neuro-symbolic algorithms for autonomous maneuver and perception reasoning. HAMERITT includes scenario generation capabilities that offer mission-relevant contextual symbolic information in addition to raw sensor data. Scenarios include symbolic descriptions for entities of interest and their relations to scene elements, as well as spatial-temporal constraints in the form of time-bounded areas of interest with prior probabilities and restricted zones within those areas. HAMERITT also features support for training distinct algorithm threads for maneuver vs. perception within an end-to-end mission run. Future work includes improving scenario realism and scaling symbolic context generation through automated workflow.

翻译：随着低成本平台和传感器载荷的出现，无人机在搜救、应急管理和执法领域的应用日益广泛。用于复杂推理的神经与符号混合人工智能方法的兴起，有望在减少人工干预的情况下进一步拓展这些应用的边界。然而，当前的无人机仿真环境缺乏适配此类混合方法的语义上下文。为弥补这一不足，HAMERITT（用于快速训练与测试的混合人工智能任务环境）提供了一个基于仿真的自主性软件框架，支持对用于自主机动与感知推理的神经符号算法进行训练、测试与验证。HAMERITT包含场景生成功能，除原始传感器数据外，还能提供与任务相关的上下文符号信息。场景包含对关注实体的符号化描述及其与场景元素的关联关系，以及以时空约束形式存在的限时关注区域（附带先验概率）和区域内的限制区。HAMERITT还支持在端到端任务执行过程中，针对机动与感知分别训练独立的算法线程。未来工作包括通过自动化工作流提升场景真实感与扩展符号上下文生成能力。