Human-Robot Interaction Using Learning from Demonstrations and a Wearable Glove with Multiple Sensors

被引:3
作者
Singh, Rajmeet [1 ]
Mozaffari, Saeed [1 ]
Akhshik, Masoud [1 ]
Ahamed, Mohammed Jalal [1 ]
Rondeau-Gagne, Simon [2 ]
Alirezaee, Shahpour [1 ]
机构
[1] Univ Windsor, Mech Automot & Mat Engn Dept, Windsor, ON N9B 3P4, Canada
[2] Univ Windsor, Dept Chem & Biochem, Windsor, ON N9B 3P4, Canada
关键词
robotic grasping; human-robot interaction; inertia; pressure; flexi sensors; wearable devices; learning from demonstration;
D O I
10.3390/s23249780
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Human-robot interaction is of the utmost importance as it enables seamless collaboration and communication between humans and robots, leading to enhanced productivity and efficiency. It involves gathering data from humans, transmitting the data to a robot for execution, and providing feedback to the human. To perform complex tasks, such as robotic grasping and manipulation, which require both human intelligence and robotic capabilities, effective interaction modes are required. To address this issue, we use a wearable glove to collect relevant data from a human demonstrator for improved human-robot interaction. Accelerometer, pressure, and flexi sensors were embedded in the wearable glove to measure motion and force information for handling objects of different sizes, materials, and conditions. A machine learning algorithm is proposed to recognize grasp orientation and position, based on the multi-sensor fusion method.
引用
收藏
页数:17
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