通过VLM-LLM推理与可达性分析实现安全的多机器人协调
Safe Multi-Robot Coordination via VLM-LLM Reasoning and Reachability Analysis
- Technical University of Munich (TUM)（慕尼黑工业大学）
- Heilbronn University of Applied Science（海尔布隆应用科学大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种集中式安全感知M2M框架，利用VLM共享感知和LLM任务分配，结合zonotope可达性验证门，实现异构机器人团队的安全协调导航，确保语义推理与形式验证的严格分离。
AI中文摘要：
在异构机器对机器（M2M）机器人系统中，当机器人具有不同的感知能力、环境感知能力和运动执行角色时，安全协调具有挑战性。本文提出了一种集中式安全感知的M2M框架，用于异构移动机器人团队（包括一个具备视觉能力的四足机器人和一个无摄像头的轮式机器人）中的协作目标导向导航。目标是引导两个平台朝向目标区域，同时避开静态和动态障碍物，并防止不安全的机器人间交互。在共享感知原则下，具备视觉能力的机器人通过MQTT代理上的集中式服务器提供语义环境感知，使无摄像头平台能够利用该共享场景表示以及自身的里程计、惯性测量单元（IMU）和状态反馈进行导航。视觉语言模型（VLM）解释视觉流，提取的语义数据被映射为保守的度量几何约束，包括膨胀的障碍物集合、安全走廊和目标区域。大语言模型（LLM）提出高层任务分配，而物理命令权限被限制在特定于机器人的zonotope可达性门内。该验证引擎在批准命令之前传播独立可达管，以评估避障、安全走廊包含和机器人间分离谓词。在清晰路径和动态障碍物场景中的在线实验表明，该流程可靠地批准安全运动，在违反约束时触发保守的重新规划或保持机动，并强制执行咨询性语义推理与经形式验证的电机执行之间的严格架构分离。
英文摘要：
Safe coordination in heterogeneous machine-to-machine (M2M) robotic systems is challenging when robots differ in sensing capabilities, environmental awareness, and motion execution roles. This paper presents a centralized safety-aware M2M framework for cooperative goal-directed navigation in a heterogeneous mobile robot team comprising a vision-capable quadruped and a camera-less robotic vehicle. The objective is to guide both platforms toward a goal region while avoiding static and dynamic obstacles and preventing unsafe inter-robot interactions. Under the principle of shared perception, the vision-capable robot provides semantic environmental awareness through a centralized server over an MQTT broker, enabling the camera-less platform to navigate using this shared scene representation alongside its own odometry, IMU, and state feedback. A vision-language model (VLM) interprets the visual stream, and the extracted semantic data is mapped into conservative metric geometric constraints, including inflated obstacle sets, safe corridors, and goal regions. A large language model (LLM) proposes high-level task allocations, while physical command authority is restricted to a robot-specific zonotope reachability gate. This verification engine propagates independent reachable tubes to evaluate obstacle avoidance, safe-corridor containment, and inter-robot separation predicates before approving commands. Online experiments across clear-path and dynamic-obstacle scenarios show that the pipeline reliably approves safe motion, triggers conservative replanning or holding maneuvers upon constraint violation, and enforces a strict architectural separation between advisory semantic reasoning and formally verified motor execution.
