This paper provides a comprehensive review of the current status, advancements, and future prospects of humanoid robots, highlighting their significance in driving the evolution of next-generation industries. By analy...This paper provides a comprehensive review of the current status, advancements, and future prospects of humanoid robots, highlighting their significance in driving the evolution of next-generation industries. By analyzing various research endeavors and key technologies, encompassing ontology structure,control and decision-making, and perception and interaction, a holistic overview of the current state of humanoid robot research is presented. Furthermore, emerging challenges in the field are identified, emphasizing the necessity for a deeper understanding of biological motion mechanisms, improved structural design,enhanced material applications, advanced drive and control methods, and efficient energy utilization. The integration of bionics, brain-inspired intelligence, mechanics, and control is underscored as a promising direction for the development of advanced humanoid robotic systems. This paper serves as an invaluable resource, offering insightful guidance to researchers in the field,while contributing to the ongoing evolution and potential of humanoid robots across diverse domains.展开更多
随着机器人技术的发展,仿人机器人在多个领域展现出应用潜力和价值。针对仿人机器人,基于机器视觉进行自主抓取研究,旨在提高仿人机器人在自然环境中的抓取适应能力和动作仿人性。在机器视觉方面,硬件采用Realsense-D435深度摄像头,利用...随着机器人技术的发展,仿人机器人在多个领域展现出应用潜力和价值。针对仿人机器人,基于机器视觉进行自主抓取研究,旨在提高仿人机器人在自然环境中的抓取适应能力和动作仿人性。在机器视觉方面,硬件采用Realsense-D435深度摄像头,利用YOLO(You Only Look Once)物体检测模型实现目标物体的识别、空间定位、深度图裁切和目标点云生成,并根据目标点云与标准点云的配准算法(ICP)获取物体的姿态,通过D-H法对机器人头部进行建模,将物体的位置和姿态由相机坐标系转换为机器人坐标系。在运动规划上,参照人手臂的抓取规律,将抓取过程分为9个基础动作:初始位、移动至预抓取位、抓取物体、提起物体、搬运物体、移动至放置位、放置物体、退出位和回到初始位,针对不同物体确定对应抓取姿态,以提高抓取成功率,根据视觉获取的抓取点和放置点,自主计算余下的关键点,并以空间弧形作为抓取轨迹,通过Matlab仿真,验证抓取过程机械臂末端运动轨迹和关节轨迹的合理性。最后进行物体抓取实验,结果表明,仿人机器人在自然环境中能够快速准确地识别和定位不同物体,并能成功进行抓取和搬运,成功率均在80%以上,并且能够兼顾动作的仿人性,验证了所提出方案的有效性。本研究可促进仿人机器人在人类日常生活中的应用和普及。展开更多
基金supported by the National Natural Science Foundation of China(62303457,U21A20482)Project funded by China Postdoctoral Science Foundation (2023M733737)the National Key R&D Program of China(2022YFB3303800)。
文摘This paper provides a comprehensive review of the current status, advancements, and future prospects of humanoid robots, highlighting their significance in driving the evolution of next-generation industries. By analyzing various research endeavors and key technologies, encompassing ontology structure,control and decision-making, and perception and interaction, a holistic overview of the current state of humanoid robot research is presented. Furthermore, emerging challenges in the field are identified, emphasizing the necessity for a deeper understanding of biological motion mechanisms, improved structural design,enhanced material applications, advanced drive and control methods, and efficient energy utilization. The integration of bionics, brain-inspired intelligence, mechanics, and control is underscored as a promising direction for the development of advanced humanoid robotic systems. This paper serves as an invaluable resource, offering insightful guidance to researchers in the field,while contributing to the ongoing evolution and potential of humanoid robots across diverse domains.
文摘随着机器人技术的发展,仿人机器人在多个领域展现出应用潜力和价值。针对仿人机器人,基于机器视觉进行自主抓取研究,旨在提高仿人机器人在自然环境中的抓取适应能力和动作仿人性。在机器视觉方面,硬件采用Realsense-D435深度摄像头,利用YOLO(You Only Look Once)物体检测模型实现目标物体的识别、空间定位、深度图裁切和目标点云生成,并根据目标点云与标准点云的配准算法(ICP)获取物体的姿态,通过D-H法对机器人头部进行建模,将物体的位置和姿态由相机坐标系转换为机器人坐标系。在运动规划上,参照人手臂的抓取规律,将抓取过程分为9个基础动作:初始位、移动至预抓取位、抓取物体、提起物体、搬运物体、移动至放置位、放置物体、退出位和回到初始位,针对不同物体确定对应抓取姿态,以提高抓取成功率,根据视觉获取的抓取点和放置点,自主计算余下的关键点,并以空间弧形作为抓取轨迹,通过Matlab仿真,验证抓取过程机械臂末端运动轨迹和关节轨迹的合理性。最后进行物体抓取实验,结果表明,仿人机器人在自然环境中能够快速准确地识别和定位不同物体,并能成功进行抓取和搬运,成功率均在80%以上,并且能够兼顾动作的仿人性,验证了所提出方案的有效性。本研究可促进仿人机器人在人类日常生活中的应用和普及。
