AI 中文总结
LUCID是一种LLM智能体编排的云机器人框架，将TP-RRM动态编排于DITL环境，结合路径规划与RRM验证器，可适应动态条件并降低规划延迟，保障机器人控制的QoS。
AI 中文摘要
云机器人依赖于大容量传感流的及时上行传输，但动态环境不断改变轨迹、活跃机器人数量及单机器人QoS的可行组合。由于现有方法将轨迹规划（TP）与无线电资源管理（RRM）表述为单一固定优化问题，无法随条件演变重新配置这些耦合决策，导致瞬态QoS违规。此外，评估轨迹相关无线冲突的计算成本过高，使得构建足够响应动态编排的大规模闭环数字孪生（DITL）测试床变得困难。我们提出LUCID，一种由大语言模型智能体编排、感知上行链路的云机器人流水线，将TP-RRM从求解固定公式转变为在DITL环境中动态编排优化问题模式。基于操作员的高层意图，LUCID将TP-RRM公式视为有界模板，其变量、目标和约束可动态配置，而SimBridge通过将大规模机器人场景转换为无线就绪的数字孪生（DT）实现重复射线追踪评估。通过将无碰撞路径规划与谱半径RRM验证器集成，LUCID识别无线瓶颈并动态重构问题模式，以高效找到经验证的可行状态。实验证实，LUCID可鲁棒适应变化的意图、活跃机器人数量及场景，而多模态代理模型FastConfigNet降低了规划延迟。
英文摘要
Cloud robotics relies on the timely uplink of high-volume sensing streams, yet dynamic environments continually shift the feasible combinations of trajectories, active-robot count, and per-robot QoS. Because existing approaches formulate trajectory planning (TP) and radio resource management (RRM) as a single fixed optimization problem, they cannot reconfigure these coupled decisions as conditions evolve, resulting in transient QoS violations. However, evolving operator intents change which quantities-such as the active-robot count and per-robot QoS-are fixed, optimized, or relaxed. Furthermore, the computational cost of evaluating trajectory-dependent wireless conflicts has made it difficult to build large-scale Digital-Twin-in-the-Loop (DITL) testbeds responsive enough for such dynamic orchestration. We present LUCID, an LLM-agent--orchestrated, uplink-aware cloud-robotics pipeline that moves TP--RRM from solving a fixed formulation to dynamically orchestrating optimization problem schemas within a DITL environment. Driven by the operator's high-level intent, LUCID treats the TP--RRM formulation as a bounded template whose variables, objectives, and constraints are dynamically configured, while SimBridge enables repeated ray-tracing evaluation by converting large-scale robotics scenes into wireless-ready DTs. By integrating collision-free path planning with a spectral-radius RRM validator, LUCID identifies wireless bottlenecks and restructures the problem schema on the fly to efficiently find the verified feasible state. Experiments confirm that LUCID robustly adapts to changing intents, active-robot counts, and scenes, while a multimodal surrogate model, FastConfigNet, reduces planning latency.
Comments10 pages, 17 figures
