Internet of Robotic Things in Surveillance

These days a new field has emerged known as IoRT which is a combination of IOT and Robotics and known as Internet of Robotic Things. Through IORT, intelligent devices can monitor events, fuse sensor data from a variety of sources, use local and distributed intelligence to determine a best course of action, and then act to control or manipulate objects in the physical world and physically moving through that world. This paper mainly focuses on application of IoRT as a surveillance robot with audio and video features in the domain of security. Keywords: IOT, Robotics, Surveillance Robot, Ardino, Sensors, Raspberry Pi, Robotic control.

NEW DESIGN OF PLC-BASED ROBOTIC CONTROL SYSTEM FOR CONCRETE PRINTING IN BUILDING CONSTRUCTION

This paper presents a new platform for the development of an open control system for a robotic arm designed for the 3D printing of buildings. This platform uses a very efficient system of automatic code generation which greatly simplifies the process of robotic arm synthesis and analysis while allowing the deployment of custom control algorithms. An experimental workplace, with a reduced and simplified robotic arm, has been developed for the purpose of testing the platform. The mechanical and electrical construction of this experimental workplace is explained. The control system platform is also introduced and data from the test results are included. Both the advantages and disadvantages are discussed at the end.

On Task-specific Autonomy in Robotic Interventions: A Multimodal Learning-based Approach for Multi-level Skill Assessment during Cyborg Catheterization

Lack of learning-based methods for characterizing the multimodal data generated during cyborg catheterization hinders the drive towards autonomous robotic control. Also, multiplexing salient features from multiple data-sources can enhance effective assessment and classification of domain skills for apt intelligent surgeon-robot (cyborg) catheterization during intravascular interventions. In this study, task-specific autonomous intervention is envisioned upon an isomorphic master-slave robotic catheter system that exhibit hand defter techniques used in Cath Labs. To drive cyborg catheterization, stacking-based deep neural network is developed for three-level skill assessment.<br>

On Task-specific Autonomy in Robotic Interventions: A Multimodal Learning-based Approach for Multi-level Skill Assessment during Cyborg Catheterization

Lack of learning-based methods for characterizing the multimodal data generated during cyborg catheterization hinders the drive towards autonomous robotic control. Also, multiplexing salient features from multiple data-sources can enhance effective assessment and classification of domain skills for apt intelligent surgeon-robot (cyborg) catheterization during intravascular interventions. In this study, task-specific autonomous intervention is envisioned upon an isomorphic master-slave robotic catheter system that exhibit hand defter techniques used in Cath Labs. To drive cyborg catheterization, stacking-based deep neural network is developed for three-level skill assessment.<br>

Biosignal-Based Human–Machine Interfaces for Assistance and Rehabilitation: A Survey

As a definition, Human–Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal-based HMIs for assistance and rehabilitation to outline state-of-the-art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full-text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever-growing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs’ complexity, so their usefulness should be carefully evaluated for the specific application.

Assessment of the training program for Versius, a new innovative robotic system for use in minimal access surgery

ObjectivesThe Versius surgical system has been developed for use in robot-assisted minimal access surgery (MAS). This study aimed to evaluate the effectiveness of the Versius training program.DesignA 3.5-day program following 10 hours of online didactic training. Participants were assessed during the technical training using the Global Evaluative Assessment of Robotic Skills (GEARS).SettingDry box exercises were conducted in classrooms, and wet lab sessions simulated an operating room environment using cadaveric specimens.ParticipantsSeventeen surgical teams participated; surgeons represented general, colorectal, obstetrics/gynecology, and urology specialties. All surgeons had previous laparoscopic MAS experience, while experience with robotics varied.Main outcomes measuresParticipants were scored on a five-point Likert Scale for each of six validated GEARS domains (depth perception, bimanual dexterity, efficiency, force sensitivity, autonomy, and robotic control). Additional metrics used to chart surgeon performance included: combined instrument path length; combined instrument angular path; and time taken to complete each task.ResultsParticipants demonstrated an overall improvement in performance during the study, with a mean GEARS Score of 21.0 (SD: 1.9) in Assessment 1 increasing to 23.4 (SD: 2.9) in Validation. Greatest improvements were observed in the depth perception and robotic control domains. Greatest differences were observed when stratifying by robotic experience; those with extensive experience consistently scored higher than those with some or no experience.ConclusionsThe Versius training program is effective; participants were able to successfully operate the system by program completion, and more surgeons achieved intermediate-level and expert-level GEARS scores in Validation compared with Assessment 1.

Robust Computed Torque Control for Uncertain Robotic Manipulatorss

This paper presents a robust control method for the trajectory control of the robotic manipulator. The standard Computed Torque Control (CTC) is an important method in the robotic control systems but its not robust to system uncertainty and external disturbance. The proposed method overcome the system uncertainty and external disturbance problems. In this paper, a robustification term has been added to the standard CTC. The stability of the proposed control method is approved by the Lyapunov stability theorem. The performance of the presented controller is tested by MATLAB-Simulink environment and is compared with different control methods to illustrate its robustness and performance.