发表机构
Robotics and AI Lab, Technological University of Uruguay（乌拉圭科技大学机器人与人工智能实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种情境感知运动框架，通过感知、理解与投影建模，结合负载、机械臂及共享区域状态预测冲突，使双移动协作机器人在共享环境中实现100%任务成功率，优于基线方法。
AI 中文摘要
本文提出了一种情境感知运动框架，用于在共享工业环境中运行的两台移动协作机器人。所提方法通过感知、理解和投影来建模情境感知，以支持运动决策。机器人位姿、负载状态、机械臂状态、共享区域占用情况、障碍物状态以及预测的机器人间冲突被用于选择安全的运动动作。该框架在仿真中实现，并在为匹配一个可行的4米乘4米物理测试区域而设计的模拟工业场景中进行了评估。所提方法与另外两种基线方法在多次试验和随机种子下进行了比较。结果表明，情境感知方法在所有场景中均实现了100%的任务成功率，而独立基线和固定优先级基线各自的总体成功率仅为33.3%。所提方法消除了共享区域冲突和安全停机，保持了最大的平均最小机器人间距离，并以最低的平均完成时间完成了任务。这些结果表明，情境感知通过结合负载状态、机械臂状态、对共享区域的推理以及对短时域冲突的预测，可以改善双机器人的运动性能。
英文摘要
This paper presents a situation aware locomotion framework for two mobile collaborative robots operating in shared industrial environments. The proposed method models situation awareness through perception, comprehension, and projection to support locomotion decisions. Robot pose, load state, manipulator state, shared zone occupancy, obstacle state, and predicted inter robot conflict were used to select safe locomotion actions. The framework was implemented in simulation and evaluated in simulated industrial scenarios designed to match a feasible 4 m by 4 m physical test area. The proposed method was compared with two other baselines over multiple trials and randomized seeds. The results show that the situation aware method achieved 100% task success across all scenarios, while the independent and fixed priority baselines each achieved 33.3% overall success. The proposed method eliminated shared zone conflicts and safety stops, maintained the largest average minimum inter robot distance, and completed the tasks with the lowest average completion time. These results indicate that situational awareness can improve the locomotion of dual robots by combining load state, manipulator state, reasoning about the shared zone, and prediction of short-horizon conflicts.
