The relevance of signal timing in human-robot collaborative manipulation

Updating design guidelines for cognitive ergonomics in human-centred collaborative robotics applications: An expert survey

Within the framework of Industry 5.0, human factors are essential for enhancing the work conditions and well-being of operators interacting with even more advanced and smart manufacturing systems and machines and increasing production performances. Nevertheless, cognitive ergonomics is often underestimated when implementing advanced industrial human-robot interaction. Thus, this work aims to systematically update, develop, and validate guidelines to assist non-experts in the early stages of the design of anthropocentric and collaborative assembly applications by focusing on the main features that have positively influenced workers' cognitive responses. A methodology for structured development has been proposed. The draft guidelines have been created starting from the outcomes of a systematic and extended screening of the scientific literature. Preliminary validation has been carried out with the help of researchers working in the field. Inputs on comprehensibility and relevance have been gathered to enhance the guidelines. Lastly, a survey was used to examine in depth how international experts in different branches can interpret such guidelines. In total, 108 responders were asked to qualitatively and quantitatively evaluate the guideline's comprehensibility and provide general comments or suggestions for each guideline. Based on the survey's results, the guidelines have been validated and some have been reviewed and re-written in their final form. The present work highlights that integrating human factors into the design of collaborative applications can significantly bolster manufacturing operations' resilience through inclusivity and system adaptability by enhancing worker safety, ergonomics, and wellbeing.

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.

Wrapping Haptic Displays Around Robot Arms to Communicate Learning

Humans can leverage physical interaction to teach robot arms. As the human kinesthetically guides the robot through demonstrations, the robot learns the desired task. While prior works focus on how the robot learns, it is equally important for the human teacher to understand what their robot is learning. Visual displays can communicate this information; however, we hypothesize that visual feedback alone misses out on the physical connection between the human and robot. In this paper we introduce a novel class of soft haptic displays that wrap around the robot arm, adding signals without affecting that interaction. We first design a pneumatic actuation array that remains flexible in mounting. We then develop single and multi-dimensional versions of this wrapped haptic display, and explore human perception of the rendered signals during psychophysic tests and robot learning. We ultimately find that people accurately distinguish single-dimensional feedback with a Weber fraction of 11.4%, and identify multi-dimensional feedback with 94.5% accuracy. When physically teaching robot arms, humans leverage the single- and multi-dimensional feedback to provide better demonstrations than with visual feedback: our wrapped haptic display decreases teaching time while increasing demonstration quality. This improvement depends on the location and distribution of the wrapped haptic display.

DMP-Based Reactive Robot-to-Human Handover in Perturbed Scenarios

While seemingly simple, handover requires joint coordinate efforts from both partners, commonly in dynamic collaborative scenarios. Practically, humans are able to adapt and react to their partner’s movement to ensure seamless interaction against perturbations or interruptions. However, literature on robotic handover usually considers straightforward scenarios. We propose an online trajectory generation method based on Dynamic Movement Primitives to enable reactive robot behavior in perturbed scenarios. Thus, the robot is able to adapt to human motion (stopping should the handover be interrupted while persisting through minor disturbances on the partner’s trajectory). Qualitative analysis is conducted to demonstrate the capability of the proposed controller with different parameter settings and against a non-reactive implementation. This analysis shows that controllers with reactive parameter settings produce robot trajectories that can be deemed as more coordinated under perturbation. Additionally, a randomized trial with participants is conducted to validate the approach by assessing the subject perception through a questionnaire while measuring task completion and robot idle time. Our method has been shown to significantly increase the subjective perception of the interaction with no statistically significant deterioration in task performance metrics under one of the two sets of parameters analyzed. This paper represents a first step towards the introduction of reactive controllers in handover tasks that explicitly consider perturbations and interruptions.

Coordinating human-robot collaboration by EEG-based human intention prediction and vigilance control

In human-robot collaboration scenarios with shared workspaces, a highly desired performance boost is offset by high requirements for human safety, limiting speed and torque of the robot drives to levels which cannot harm the human body. Especially for complex tasks with flexible human behavior, it becomes vital to maintain safe working distances and coordinate tasks efficiently. An established approach in this regard is reactive servo in response to the current human pose. However, such an approach does not exploit expectations of the human's behavior and can therefore fail to react to fast human motions in time. To adapt the robot's behavior as soon as possible, predicting human intention early becomes a factor which is vital but hard to achieve. Here, we employ a recently developed type of brain-computer interface (BCI) which can detect the focus of the human's overt attention as a predictor for impending action. In contrast to other types of BCI, direct projection of stimuli onto the workspace facilitates a seamless integration in workflows. Moreover, we demonstrate how the signal-to-noise ratio of the brain response can be used to adjust the velocity of the robot movements to the vigilance or alertness level of the human. Analyzing this adaptive system with respect to performance and safety margins in a physical robot experiment, we found the proposed method could improve both collaboration efficiency and safety distance.