Martinahmed1659

Finally, qualitative and quantitative experiments verify the feasibility of our method and present a large-scale evaluation of SpDF with other seven point cloud sampling algorithms, in the context of the 3D registration problem using the Iterative Closest Point (ICP) algorithm on real-world datasets. Results show that a compression ratio up to 97 % can be achieved when accepting a registration error within the range accuracy of the sensor, here for large scale environments of less than 2 cm.Optical see-through (OST) augmented reality head-mounted displays are quickly emerging as a key asset in several application fields but their ability to profitably assist high precision activities in the peripersonal space is still sub-optimal due to the calibration procedure required to properly model the user's viewpoint through the see-through display. In this work, we demonstrate the beneficial impact, on the parallax-related AR misregistration, of the use of optical see-through displays whose optical engines collimate the computer-generated image at a depth close to the fixation point of the user in the peripersonal space. To estimate the projection parameters of the OST display for a generic viewpoint position, our strategy relies on a dedicated parameterization of the virtual rendering camera based on a calibration routine that exploits photogrammetry techniques. We model the registration error due to the viewpoint shift and we validate it on an OST display with short focal distance. The results of the tests demonstrate that with our strategy the parallax-related registration error is submillimetric provided that the scene under observation stays within a suitable view volume that falls in a ±10 cm depth range around the focal plane of the display. This finding will pave the way to the development of new multi-focal models of OST HMDs specifically conceived to aid high-precision manual tasks in the peripersonal space.Recently, with the increased number of robots entering numerous manufacturing fields, a considerable wealth of literature has appeared on the theme of physical human-robot interaction using data from proprioceptive sensors (motor or/and load side encoders). Most of the studies have then the accurate dynamic model of a robot for granted. In practice, however, model identification and observer design proceeds collision detection. To the best of our knowledge, no previous study has systematically investigated each aspect underlying physical human-robot interaction and the relationship between those aspects. In this paper, we bridge this gap by first reviewing the literature on model identification, disturbance estimation and collision detection, and discussing the relationship between the three, then by examining the practical sides of model-based collision detection on a case study conducted on UR10e. We show that the model identification step is critical for accurate collision detection, while the choice of the observer should be mostly based on computation time and the simplicity and flexibility of tuning. It is hoped that this study can serve as a roadmap to equip industrial robots with basic physical human-robot interaction capabilities.Human intention detection is fundamental to the control of robotic devices in order to assist humans according to their needs. This paper presents a novel approach for detecting hand motion intention, i.e., rest, open, close, and grasp, and grasping force estimation using force myography (FMG). The output is further used to control a soft hand exoskeleton called an SEM Glove. In this method, two sensor bands constructed using force sensing resistor (FSR) sensors are utilized to detect hand motion states and muscle activities. Upon placing both bands on an arm, the sensors can measure normal forces caused by muscle contraction/relaxation. Afterwards, the sensor data is processed, and hand motions are identified through a threshold-based classification method. The developed method has been tested on human subjects for object-grasping tasks. The results show that the developed method can detect hand motions accurately and to provide assistance w.r.t to the task requirement.Gaze behavior is an important social signal between humans as it communicates locations of interest. People typically orient their attention to where others look as this informs about others' intentions and future actions. Studies have shown that humans can engage in similar gaze behavior with robots but presumably more so when they adopt the intentional stance toward them (i.e., believing robot behaviors are intentional). learn more In laboratory settings, the phenomenon of attending toward the direction of others' gaze has been examined with the use of the gaze-cueing paradigm. While the gaze-cueing paradigm has been successful in investigating the relationship between adopting the intentional stance toward robots and attention orienting to gaze cues, it is unclear if the repetitiveness of the gaze-cueing paradigm influences adopting the intentional stance. Here, we examined if the duration of exposure to repetitive robot gaze behavior in a gaze-cueing task has a negative impact on subjective attribution of intentionaod of adopting the intentional stance toward a robot.Wearable robots (WRs) are increasingly moving out of the labs toward real-world applications. In order for WRs to be effectively and widely adopted by end-users, a common benchmarking framework needs to be established. In this article, we outline the perspectives that in our opinion are the main determinants of this endeavor, and exemplify the complex landscape into three areas. The first perspective is related to quantifying the technical performance of the device and the physical impact of the device on the user. The second one refers to the understanding of the user's perceptual, emotional, and cognitive experience of (and with) the technology. The third one proposes a strategic path for a global benchmarking methodology, composed by reproducible experimental procedures representing real-life conditions. We hope that this paper can enable developers, researchers, clinicians and end-users to efficiently identify the most promising directions for validating their technology and drive future research efforts in the short and medium term.