面向受操作约束的无人机-无人车团队的协作规划：基于掩码误差有界的神经符号扩散模型
Collaborative Planning for UAV-UGV Teams under Operational Constraints via Masked Error-bounded Neuro-Symbolic Diffusion
AI总结：
本文提出MEND模型，将协作规划建模为条件掩码生成过程，通过神经符号范式与误差有界推理调度提升无人机-无人车团队在操作约束下的协作规划性能，经多场景实验验证其有效性。
摘要：
受操作约束的协作规划对异构机器人团队执行大规模任务至关重要：无人机（UAV）可实现快速区域覆盖，但受机载能量限制；无人车（UGV）可作为移动充电站，但受道路网络通行性约束。有效协作需在执行过程中遵守操作约束，同时支持并行、协调的动作。掩码扩散模型（MDMs）虽通过令牌去掩蔽展现出潜力，但存在两个关键局限：熵引导调度假设分布对齐，且依赖可微约束进行扩散时间优化。本文提出掩码误差有界的神经符号扩散模型（MEND），将协作规划建模为条件掩码生成过程，为无人机-无人车团队生成上下文感知的动作序列。MEND引入神经符号扩散范式，通过逻辑诱导的可接受性指标对学习到的动作分布进行重加权，在执行时约束条件掩码扩散策略，从而确保符合操作约束；同时引入执行时的误差有界推理调度，通过选择约束感知动作分布上预测熵最小的位置构建去掩蔽调度，提升受操作约束下协作规划的最优性。在仿真、真实地图及物理机器人团队上开展的大量实验结果表明，MEND推进了受操作约束的无人机-无人车协作规划。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
