发表机构
School of Computing, Macquarie University; Department of Computer Science, University of California（麦考瑞大学计算机学院; 加州大学计算机科学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出NEUROSYMLAND系统，结合轻量感知与符号推理进行着陆点评估，在模拟和硬件在环测试中优于基线方法，实现边缘部署下的鲁棒性与可解释性。
AI 中文摘要
在非结构化环境中安全着陆点评估仍然是自主无人机部署的关键挑战，因为纯视觉学习方法在地形变化下性能下降，且在安全决策中透明度有限。我们提出NEUROSYMLAND，一种神经符号着陆点评估系统，将轻量感知与显式安全推理相结合。该框架从机载视觉输入构建概率语义场景图，并使用捕获地形平坦度、障碍物间隙和空间一致性的符号约束评估候选着陆区域，从而在感知不确定性下实现结构化推理，同时保持边缘可执行性。在涵盖不同地形的72个模拟着陆场景中，NEUROSYMLAND实现了61次成功评估，优于四个竞争基线（37-57次成功）。为评估可部署性，我们进一步进行了100次硬件在环试验，随机初始姿态，分析端到端延迟、阶段执行时间以及CPU/GPU利用率、内存占用和功耗等系统级指标。结果表明，在有限的边缘资源使用下，鲁棒性和可解释性得到改善。性能分析显示，符号推理仅占端到端延迟的一小部分，而主要计算成本来自感知和PSSG构建。这些结果证明了在边缘受限的无人机硬件上部署着陆点评估栈的可行性，所有源代码、数据集、提示和符号规则细化示例均在开源仓库中发布。
英文摘要
Safe landing-site assessment in unstructured environments remains a key challenge for autonomous UAV deployment, as vision-only learning approaches often degrade under terrain variability and provide limited transparency in safety decisions. We present NEUROSYMLAND, a neuro-symbolic landing-site assessment system that integrates lightweight perception with explicit safety reasoning. The framework constructs a probabilistic semantic scene graph from onboard visual input and evaluates candidate landing regions using symbolic constraints capturing terrain flatness, obstacle clearance, and spatial consistency, enabling structured reasoning under perceptual uncertainty while maintaining edge-feasible execution. Across 72 simulated landing scenarios spanning diverse terrains, NEUROSYMLAND achieves 61 successful assessments, outperforming four competitive baselines (37-57 successes). To evaluate deployability, we further conduct 100 hardware-in-the-loop trials with randomized initial poses, profiling end-to-end latency, stage-wise execution time, and system-level metrics including CPU/GPU utilization, memory footprint, and power consumption. Results demonstrate improved robustness and interpretability with bounded edge-resource usage. Profiling shows that symbolic reasoning contributes only a small fraction of end-to-end latency, while the main computational cost arises from perception and PSSG construction. These results demonstrate the feasibility of deploying the landing-site assessment stack on edge-constrained UAV hardware, and all source code, datasets, prompts, and symbolic rule refinement examples are released in an open-source repository
CommentsAccepted to the IROS 2026
Journal refroceedings of the ACM on Software Engineering, Vol. 3, FSE, Article FSE146, July 2026. Article FSE146 (July 2026)
DOI：10.1145/3808153
