Faster R-CNN-based Decision Making in a Novel Adaptive Dual-Mode Robotic Anchoring System

This paper proposes a novel adaptive anchoring module that can be integrated into robots to enhance their mobility and manipulation abilities. The module can deploy a suitable mode of attachment, via spines or vacuum suction, to different contact surfaces in response to the textural properties of the surfaces. In order to make a decision on the suitable mode of attachment, an original dataset of 100 images of outdoor and indoor surfaces was enhanced using a WGAN-GP generating an additional 200 synthetic images. The enhanced dataset was then used to train a visual surface examination model using Faster R-CNN. The addition of synthetic images increased the mean average precision of the Faster R-CNN model from 81.6% to 93.9%. We have also conducted a series of load tests to characterize the module’s strength of attachments. The results of the experiments indicate that the anchoring module can withstand an applied detachment force of around 22N and 20N when attached using spines and vacuum suction on the ideal surfaces, respectively

ROSIC: Enhancing secure and accessible robot control through open-source instant messaging platforms

Ensuring secure communication and seamless accessibility remains a primary challenge in controlling robots remotely. The authors propose a novel approach that leverages open-source instant messaging platforms to overcome the complexities and reduce costs associated with implementing a secure and user-centred communication system for remote robot control named Robot Control System using Instant Communication (ROSIC). By leveraging features, such as real-time messaging, group chats, end-to-end encryption and cross-platform support inherent in the majority of instant messenger platforms, we have developed middleware that establishes a secure and efficient communication system over the Internet. By using instant messaging as the communication interface between users and robots, ROSIC caters to non-technical users, making it easier for them to control robots. The architecture of ROSIC enables various scenarios for robot control, including one user controlling multiple robots, multiple users controlling one robot, multiple robots controlled by multiple users, and one user controlling one robot. Furthermore, ROSIC facilitates the interaction of multiple robots, enabling them to interoperate and function collaboratively as a swarm system by providing a unified communication platform that allows for seamless exchange of data and commands. Telegram was specifically chosen as the instant messaging platform by the authors due to its open-source nature, robust encryption, compatibility across multiple platforms and interactive communication capabilities through channels and groups. Notably, the ROSIC is designed to communicate effectively with robot operating system (ROS)-based robots to enhance our ability to control them remotely

Autonomous decision making in a bioinspired adaptive robotic anchoring module

This paper proposes a bioinspired adaptive anchoring module that can be integrated into robots to enhance their mobility and manipulation abilities. The design of the module is inspired by the structure of the mouth in Chilean lamprey (Mordacia lapicida) where a combination of suction and several arrays of teeth with different sizes around the mouth opening is used for catching preys and anchoring onto them. The module can deploy a suitable mode of attachment, via teeth or vacuum suction, to different contact surfaces in response to the textural properties of those surfaces. In order to make a decision on the suitable mode of attachment, an original dataset of 500 images of outdoor and indoor surfaces was used to train a visual surface examination model using YOLOv3; a virtually real-time object detection algorithm. The mean average precision of the trained model was calculated to be 91%. We have conducted a series of pull-out tests to characterize the module’s strength of attachments. The results of the experiments indicate that the anchoring module can withstand an applied detachment force of up to 70N and 30N when attached using teeth and vacuum suction, respectively

CVOCR: Context Vision OCR

Optical Character Recognition (OCR) technologies are crucial for automated information extraction across various domains. However, the intricate layouts and diverse text properties often found on different products can complicate accurate data retrieval and categorization. This paper introduces Context Vision OCR (CVOCR), a versatile framework designed to address the proposed challenges using advanced image processing and text analysis techniques. While CVOCR is applicable to any OCR-related application, this paper focuses on pharmaceutical items as a case study due to the stringent accuracy requirements and the complexity of medicine packaging. The CVOCR algorithm is developed based on the integration of the Fast Super-Resolution Convolutional Neural Network (FSRCNN) for enhanced image clarity, LayoutLMv2 for spatial layout understanding, Tesseract OCR for robust character recognition, and GPT-Neo for advanced contextual analysis. The strategic integration of these components form a cohesive system that significantly improves text detection and interpretation accuracy. We demonstrate the efficacy of the CVOCR system through testing on various pharmaceutical products, where it consistently outperforms Tesseract OCR

Vision-based self-adaptive gripping in a trimodal robotic sorting end-effector

Recyclable waste management, which includes sorting as a key process, is a crucial component of maintaining a sustainable ecosystem. The use of robots in sorting could significantly facilitate the production of secondary raw materials from waste in the sense of a recycling economy. However, due to the complex and heterogeneous types of the recyclable items, the conventional robotic gripping end-effectors, which typically come with a fixed structure, are unlikely to hold onto the full range of items to enable separation and recycling. To this end, a trimodal adaptive end-effector is proposed that can be integrated with robotic manipulators to improve their gripping versatility. The end-effector can deploy effective modes of gripping to different objects in response to their size and porosity via gripping mechanisms based on Nano Polyurethane (PU) adhesive gels, pumpless vacuum suction, and radially deployable claws. While the end-effector’s mechanical design allows the three gripping modes to be deployed independently or in conjunction with one another, this work aims at deploying modes that are effective for gripping onto the recyclable item. In order to decide on the suitable modes of gripping, a real-time vision system is designed to measure the size and porosity of the recyclable items and advise on a suitable combination of gripping modes to be deployed. Integrated current sensors provide an indication of successful gripping and releasing of the recyclable items. The results of the experiments confirmed the ability of our vision-based approach in identifying suitable gripping modes in real-time, the deployment of the relevant mechanisms and successful gripping onto a maximum of 84.8% (single-mode), 90.9% (dual-mode) and 96.9% (triple-mode) of a specified set of recyclable items

Histological and Radiological Evaluation of Low-Intensity Pulsed Ultrasound Versus Whole Body Vibration on Healing of Mandibular Bone Defects in Rats

Background and Objectives: Mechanical stimulation can improve the structural properties of the fracture site and induce the differentiation of different cell types for bone regeneration. This study aimed to compare the effect of low-intensity pulsed ultrasound stimulation (LIPUS) versus whole body vibration (WBV) on healing of mandibular bone defects. Materials and Methods: A mandibular defect was created in 66 rats. The rats were randomly divided into two groups of rats. Each group was subdivided randomly by three groups (n = 11) as follows: (I) control group, (II) treatment with LIPUS, and (III) treatment with WBV. The radiographic changes in bone density, the ratio of lamellar bone to the entire bone volume, the ratio of the newly formed bone to the connective tissue and inflammation grade were evaluated after 1 and 2 months. Results: LIPUS significantly increased the radiographic bone density change compared to the control group at the first and second month postoperatively (p < 0.01). WBV only significantly increased the bone density compared to the control group at the second month after the surgery (p < 0.01). Conclusions: Application of LIPUS and WBV may enhance the regeneration of mandibular bone defects in rats. Although LIPUS and WBV are effective in mandibular bone healing, the effects of LIPUS are faster and greater than WBV