cs.ROOct 13, 2026

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

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

Organizations: University of Massachusetts Amherst · U.S. Army DEVCOM Army Research Laboratory

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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.

Figures & tables

Figure 1: As a collaborative team, traversability-constrained UGVs serve as mobile recharging stations for energy-constrained UAVs, enabling parallel operation while satisfying energy, traversability, and recharging constraints.
Figure 2: MEND formulates collaborative planning as a conditional masked generative process for context-aware joint action generation. During execution, a neuro-symbolic diffusion paradigm enforces operational constraints via FOL-induced admissibility indicators, while an error-bounded inference scheduling minimizes entropy to improve planning optimality.
Figure 3: Unity-ROS results with Starling UAVs and Warthog UGVs under different missions. Green circles denote recharge locations, colored curves UAV trajectories, and black lines UGV paths.
Figure 4: Results for collaborative planning under operational constraints. The first column shows the team configuration and a recharge event, while the remaining columns show different timestamps.
Figure 5: Qualitative results in unseen environments with different depots and road networks: a city scenario with 2 UAV-2 UGV and 15 tasks, and a suburban scenario with 4 UAV-2 UGV and 45 tasks.
Figure 6: MEND demonstrates generalizability on a 3D LiDAR-scanned map of a real, unseen university campus. This validation scenario consists of 15 tasks executed by 4 UAV-2 UGV.
Figure 7: Qualitative results of MEND in real-world mixed-reality case studies using two Crazyflie UAVs and two Limo UGVs controlled via ROS2.
Figure 8: Runtime analysis.
Figure 9: Ablation on neuro-symbolic (NS) and entropy-guided scheduling (EGS). Dashed lines denote plans violating operational constraints.

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