论文/arXiv 2610.18850·15 天前

通过 Masked Error-bounded Neuro-Symbolic Diffusion 实现操作约束下的 UAV-UGV 团队协同规划

针对受能量与道路可达性约束的UAV-UGV协同规划，MEND以神经符号掩码扩散生成动作序列，并在仿真、真实地图和实体团队验证。

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原文链接：Collaborative Planning for UAV-UGV Teams under Operational Constraints via Masked Error-bounded Neuro-Symbolic Diffusion（arXiv 2610.18850 · 发布日期：2026-10-13）

Collaborative Planning for UAV-UGV Teams under Operational Constraints via Masked Error-bounded Neuro-Symbolic Diffusion

针对受能量与道路可达性约束的UAV-UGV协同规划，MEND以神经符号掩码扩散生成动作序列，并在仿真、真实地图和实体团队验证。

核心要点

• 大规模任务中的异构机器人协同需在执行时满足操作约束，并支持并行协调动作。
• MEND将协同规划表述为条件掩码生成过程，生成面向上下文的团队动作序列。
• 方法用逻辑诱导的可采纳性指标重加权动作分布，并以最小预测熵构建解掩码调度。
• 实验在仿真、真实世界地图和物理机器人团队上表明MEND推进了受约束协同规划。

验证范围与局限：验证范围包括仿真、真实世界地图和物理机器人团队上的UAV-UGV协同规划。

英文摘要 (Abstract)

Collaborative planning under operational constraints is essential for heterogeneous robot teams in large-scale missions. Unmanned Aerial Vehicles (UAVs) provide rapid area coverage but are limited by onboard energy, while Unmanned Ground Vehicles (UGVs) can serve as mobile recharging stations yet are constrained by road-network traversability. Effective collaboration must enforce operational constraints during execution while enabling parallel, coordinated actions. Masked Diffusion Models (MDMs) have shown promise through token unmasking but have two key limitations: entropy-guided scheduling assumes distributional alignment and reliance on differentiable constraints for diffusion-time optimization. We propose Masked Error-bounded Neuro-Symbolic Diffusion (MEND), which formulates collaborative planning as a conditional masked generative process to generate context-aware action sequences for UAV-UGV teams. MEND introduces a neuro-symbolic diffusion paradigm that constrains conditional masked diffusion policies at execution by reweighting the learned action distribution with a logic-induced admissibility indicator, thereby enforcing compliance with operational constraints. MEND introduces an error-bounded inference scheduling at execution that constructs the unmasking schedule by selecting positions with minimal predictive entropy over the constraint-aware action distribution, enhancing collaborative planning optimality under operational constraints. Extensive experimental results in simulation, on real-world maps, and with physical robot teams demonstrate that MEND advances UAV-UGV collaborative planning under operational constraints.

作者：Zihao Deng, Qianhuang Li, Maggie Wigness, John Rogers, Donghyun Kim, Hao Zhang

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2610.18850：Collaborative Planning for UAV-UGV Teams under Operational Constraints via Masked Error-bounded Neuro-Symbolic Diffusion访问 2026-10-21