Adaptive Human Force Scaling via Admittance Control for Physical Human-Robot Interaction

HM-Array: A Novel Haptic Magnetism-Based Leader-Follower Platform for Minimally Invasive Robotic Surgery

Ensuring the safety and authenticity of haptic feedback is crucial in the domain of surgical operations, particularly in procedures like Natural Orifice Transluminal Endoscopic Surgery (NOTES) and Minimally Invasive Robotic Surgery (MIRS). To enhance the control efficiency of the robotic operating console, we propose a haptic magnetism-based array (HM-Array). This system employs a solenoid array and a detection stylus to achieve controller localization without the need for additional sensors, while simultaneously generating haptic effects. The device effectively controls the surgical robot's pose through a localization-haptic combined loopback. The entire system is scheduled on a finite state machine (FSM), seamlessly fusing localization and haptic generation. Psychometric evaluations conducted through user studies have demonstrated the device's precision and accuracy. Teleoperation experimental results further confirm its potential value in surgical treatments and broader medical haptic applications.

Human-robot planar co-manipulation of extended objects: data-driven models and control from human-human dyads

Human teams are able to easily perform collaborative manipulation tasks. However, simultaneously manipulating a large extended object for a robot and human is a difficult task due to the inherent ambiguity in the desired motion. Our approach in this paper is to leverage data from human-human dyad experiments to determine motion intent for a physical human-robot co-manipulation task. We do this by showing that the human-human dyad data exhibits distinct torque triggers for a lateral movement. As an alternative intent estimation method, we also develop a deep neural network based on motion data from human-human trials to predict future trajectories based on past object motion. We then show how force and motion data can be used to determine robot control in a human-robot dyad. Finally, we compare human-human dyad performance to the performance of two controllers that we developed for human-robot co-manipulation. We evaluate these controllers in three-degree-of-freedom planar motion where determining if the task involves rotation or translation is ambiguous.

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

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.