Current Issue : April-June Volume : 2024 Issue Number : 2 Articles : 5 Articles
Older individuals prefer to maintain their autonomy while maintaining social connection and engagement with their family, peers, and community. Though individuals can encounter barriers to these goals, socially assistive robots (SARs) hold the potential for promoting aging in place and independence. Such domestic robots must be trusted, easy to use, and capable of behaving within the scope of accepted social norms for successful adoption to scale. We investigated perceived associations between robot sociability and trust in domestic robot support for instrumental activities of daily living (IADLs). In our multi-study approach, we collected responses from adults aged 65 years and older using two separate online surveys (Study 1, N = 51; Study 2, N = 43). We assessed the relationship between perceived robot sociability and robot trust. Our results consistently demonstrated a strong positive relationship between perceived robot sociability and robot trust for IADL tasks. These data have design implications for promoting robot trust and acceptance of SARs for use in the home by older adults....
Home service robots operating indoors, such as inside houses and offices, require the real-time and accurate identification and location of target objects to perform service tasks efficiently. However, images captured by visual sensors while in motion states usually contain varying degrees of blurriness, presenting a significant challenge for object detection. In particular, daily life scenes contain small objects like fruits and tableware, which are often occluded, further complicating object recognition and positioning. A dynamic and real-time object detection algorithm is proposed for home service robots. This is composed of an image deblurring algorithm and an object detection algorithm. To improve the clarity of motion-blurred images, the DA-Multi-DCGAN algorithm is proposed. It comprises an embedded dynamic adjustment mechanism and a multimodal multiscale fusion structure based on robot motion and surrounding environmental information, enabling the deblurring processing of images that are captured under different motion states. Compared with DeblurGAN, DA-Multi-DCGAN had a 5.07 improvement in Peak Signal-to-Noise Ratio (PSNR) and a 0.022 improvement in Structural Similarity (SSIM). An AT-LI-YOLO method is proposed for small and occluded object detection. Based on depthwise separable convolution, this method highlights key areas and integrates salient features by embedding the attention module in the AT-Resblock to improve the sensitivity and detection precision of small objects and partially occluded objects. It also employs a lightweight network unit Lightblock to reduce the network’s parameters and computational complexity, which improves its computational efficiency. Compared with YOLOv3, the mean average precision (mAP) of AT-LI-YOLO increased by 3.19%, and the detection precision of small objects, such as apples and oranges and partially occluded objects, increased by 19.12% and 29.52%, respectively. Moreover, the model inference efficiency had a 7 ms reduction in processing time. Based on the typical home activities of older people and children, the dataset Grasp-17 was established for the training and testing of the proposed method. Using the TensorRT neural network inference engine of the developed service robot prototype, the proposed dynamic and real-time object detection algorithm required 29 ms, which meets the real-time requirement of smooth vision....
Dragon fruit is a tropical fruit with significant potential for consumers and producers. The quality assurance of this high-value product is crucial to satisfy consumer expectations. The quality of imported dragon fruit after storage may deteriorate due to inappropriate storage conditions. The firmness of dragon fruit is an essential parameter to estimate its conditions, and it is usually measured by destructive testing. The objective of the present study is to develop and test a non-destructive robotic sensor for assessing dragon fruit quality related to texture deterioration. Sixty white-fresh dragon fruits obtained from a store were divided in two sets of thirty fruits and stored 48 h at different conditions (cold and room storage) to produce deteriorated and consumer-acceptable fruits. First, the fruit samples were assessed non-destructive with the force sensor of a collaborative robot while they were touched. The robot tool is a pad capable of adapting and copying fruit shapes while controlling its hardness with the jamming transition of its internal granular fill. Second, the fruits were evaluated with destructive tests such as fruit firmness, flesh firmness, and soluble solid content. The procedure followed to produce deteriorated and acceptable fruits were confirmed. A discriminant analysis was carried out to segregate the fruit between the two categories according to the non-destructive variables extracted from the sensor. The variables obtained from the robotic first slope (S1) and the difference between the maximum value and the first overshoot (Os) were significant predictors for the separation in the two quality categories. Promising results were obtained with 77.50% of well classified fruit from the model data set, and 84.21% from the validation data set. The use of the robot could be an efficient tool in evaluating the quality of dragon fruit. This process may lead to substantial savings, particularly considering the elevated cost associated with the importation of tropical fruits into the European market....
The Uncanny Valley hypothesis implies that people perceive a subjective border between human and robot faces. The robot–human border refers to the level of human-like features that distinguishes humans from robots. However, whether people’s perceived anthropomorphism and robot–human borders are consistent across different robot occupations remains to be explored. This study examined the robot–human border by analyzing the human photo proportion represented by the point of subjective equality in three image classification tasks. Stimulus images were generated by morphing a robot face photo and one each of four human photos in systematically changed proportions. Participants classified these morphed images in three different robot occupational conditions to explore the effect of changing robot jobs on the robot–human border. The results indicated that robot occupation and participant age and gender influenced people’s perceived anthropomorphism of robots. These can be explained by the implicit link between robot job and appearance, especially in a stereotyped context. The study suggests that giving an expected appearance to a robot may reproduce and strengthen a stereotype that associates a certain appearance with a certain job....
In the implementation of robot motion control, complex kinematic computations consume too much central processing unit (CPU) time and affect the responsiveness of robot motion. To solve this problem, this paper proposes a parallel method for solving kinematic equations of articulated robots based on the coordinate rotation digital computer (CORDIC) algorithm. The method completes the fast calculation of the transcendental function based on the CORDIC algorithm, adopts the tree structure method to optimize the key computational paths of forward and inverse solutions, and designs a parallel pipeline to realize the low latency and high throughput of the kinematic equations. The experiments of the proposed method are validated based on the fieldprogrammable gate array (FPGA) hardware experimental platform, and the experimental results demonstrate that the computational time to complete the entire kinematic equations is 4.68 μs, of which the computational time for the kinematic positive solution is 0.52 μs and the computational time for the kinematic inverse solution is 4.16 μs....
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