Mobile manipulators are increasingly deployed in industrial settings, such as material handling and workpiece loading, where they must safely interact with humans while efficiently completing tasks. Existing motion planning methods for mobile manipulators often struggle to ensure both safety and efficiency in dynamic human-robot interaction environments. This paper proposes a Safety Posture Field framework that addresses these limitations by firstly predicting human motion trends using the improved Long Short-Term Memory neural network and applying these predictions to potential field calculations for both the mobile platform and the robotic arm. During different stages of human-robot interaction, the mobile manipulator places varying emphasis on safety and efficiency while in motion. Additionally, when the robotic arm executes operations, a platform-arm coupling motion strategy is introduced when the potential field detects risks of singularity or local optima, preventing the robotic arm from becoming unstable or failing to reach the target pose in time. This strategy enhances the system's flexibility and operational stability. Comparative experiments in simulation and real-world settings confirm the ability of the framework to maintain high safety standards while improving task efficiency, making it suitable for industrial Human-Robot Interaction applications.

中文翻译：

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基于人机交互趋势和平台-手臂耦合运动的移动机械手安全姿态场框架


移动机械手越来越多地部署在工业环境中，例如物料搬运和工件装载，它们必须在高效完成任务的同时安全地与人类互动。现有的移动机械手运动规划方法通常难以在动态人机交互环境中确保安全性和效率。本文提出了一个安全姿势场框架，该框架首先使用改进的长短期记忆神经网络预测人体运动趋势，并将这些预测应用于移动平台和机械臂的势场计算，从而解决了这些限制。在人机交互的不同阶段，移动机械手对运动中的安全性和效率的重视程度各不相同。此外，当机械臂执行操作时，当势场检测到奇异性或局部最优风险时，引入平台-臂耦合运动策略，防止机械臂变得不稳定或无法及时到达目标位姿。这种策略增强了系统的灵活性和运行稳定性。仿真和真实环境中的比较实验证实了该框架在提高任务效率的同时保持高安全标准的能力，使其适用于工业人机交互应用。