影响因子：9.1
所属单位：北京航空航天大学
发表刊物：Robotics and Computer-Integrated Manufacturing,
关键字：Human-robot interaction，Safety posture ffeld，Mobile manipulators，Coupled movement
摘要：Mobile manipulators are increasingly deployed in industrial settings, such as material handling and workpiece loading, where they must safely interact with humans while efffciently completing tasks. Existing motion planning methods for mobile manipulators often struggle to ensure both safety and efffciency in dynamic humanrobot interaction environments. This paper proposes a Safety Posture Field framework that addresses these limitations by ffrstly predicting human motion trends using the improved Long Short-Term Memory neural network and applying these predictions to potential ffeld 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 efffciency while in motion. Additionally, when the robotic arm executes operations, a platform-arm coupling motion strategy is introduced when the potential ffeld 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 ffexibility and operational stability. Comparative experiments in simulation and realworld settings conffrm the ability of the framework to maintain high safety standards while improving task efffciency, making it suitable for industrial Human-Robot Interaction applications.
论文类型：期刊论文
一级学科：机械工程
文献类型：期刊
是否译文：否
发表时间：2025-02-05
收录刊物：SCI