A Novel Human Intention Prediction Approach Based on Fuzzy Rules through Wearable Sensing in Human–Robot Handover

Abstract

With the use of collaborative robots in intelligent manufacturing, human–robot interaction has become more important in human–robot collaborations. Human–robot handover has a huge impact on human–robot interaction. For current research on human–robot handover, special attention is paid to robot path planning and motion control during the handover process; seldom is research focused on human handover intentions. However, enabling robots to predict human handover intentions is important for improving the efficiency of object handover. To enable robots to predict human handover intentions, a novel human handover intention prediction approach was proposed in this study. In the proposed approach, a wearable data glove and fuzzy rules are firstly used to achieve faster and accurate human handover intention sensing (HIS) and human handover intention prediction (HIP). This approach mainly includes human handover intention sensing (HIS) and human handover intention prediction (HIP). For human HIS, we employ wearable data gloves to sense human handover intention information. Compared with vision-based and physical contact-based sensing, wearable data glove-based sensing cannot be affected by visual occlusion and does not pose threats to human safety. For human HIP, we propose a fast handover intention prediction method based on fuzzy rules. Using this method, the robot can efficiently predict human handover intentions based on the sensing data obtained by the data glove. The experimental results demonstrate the advantages and efficacy of the proposed method in human intention prediction during human–robot handover.

Teleoperated Grasping Using Data Gloves Based on Fuzzy Logic Controller

“…In Figure 6, left_ring represents the standard value of the left hand's ring finger flexion; right_x represents the standard values of the right hand's finger flexions, including the right hand's thumb PIP joint (right_thumb_PIP), right hand's index finger (right_index), right hand's middle finger (right_middle), right hand's ring finger (right_ring), and right hand's little finger (right_little); rt_mcp represents the right hand's thumb MCP joint; velj represents the joint velocity (rad/s) sent to Joint j of the robot arm, j ∈ [1,2,3,4,5].…”

“…le f t_ring < 0.5 and 0.5 < right_x −(right_x − 0.5), 0.5 < le f t_ring and 0.5 < right_x 0, else (4) where left_ring represents the standard value of left hand's ring finger flexion; right_x represents the standard values of right hand's finger flexions, including right thumb (right_thumb), right index finger (right_index), right middle finger (right_middle), right ring finger (right_ring), and right little finger (right_little); velj represents the joint velocity (rad/s) sent to Joint j of the robot arm, j ∈ [1,2,3,4,5].…”

“…Operators control distant robots through devices like joysticks and the robots leverage human cognition, creativity, and experience to complete tasks in complex environments. Human intervention in the system can compensate for deficiencies in robot control, sensing, and artificial intelligence, among others [4][5][6]. As of today, many scholars have researched teleoperation technology.…”