Biomechanics

Motion control platforms have various applications in the manufacturing and automation industries. Different literature provides multiple issues related to the kinematics and dynamics of self-guided robots for transportation regarding platform balancing. Self-balancing platforms are utilized in many deliveries, stabilization, and transportation systems, and they are especially well suited for outdoor activities when the ground surface is not flat or structured. This paper describes developing a control technique for a self-balancing platform using the 3-RCC spherical parallel manipulator. This mechanism was designed to support an AGV (Automated Guided Vehicle) for transporting and lifting heavy weights for industrial applications. The AGV carries a robotic arm on top for different tasks. When the AGV encounters a steep slope or a rough surface, the AGV tilts, and the robotic arm’s performance is significantly affected. So, this study gives a solution to avoid these circumstances with a novel approach for the platform’s self-balancing mechanism consisting of a 3-RCC spherical parallel manipulator. Real-time stabilization and kinematics analysis methods are used to achieve the self-balancing system of the platform. When both methods are observed through different tilting angles for automation stability, Kinematic analysis performs more efficiently with less time duration when compared with the real-time stabilization method.

Collaborative robots are machines that work hand in hand with humans; or as the name suggests, collaborate with them in a specific workspace. These robots are not enclosed in confined safety zones like traditional robots, as they interact very closely with humans. Though this is the case, appropriate measures are captivated while designing these robots considering human safety. These robots are well-versed in adapting to changes and frequent upgrades. They are flexible enough to carry out complex tasks. Due to these abilities, they become a significant asset in the manufacturing field. It’s been many years now since cobots are introduced in the industry sector. So, this is the right time to review various applications of cobots in manufacturing. First, the paper starts with a brief introduction followed by an extensive literature review which was structured after reviewing 76 research papers and articles. It ends with some essential conclusions. This paper discusses the diverse applications of cobots used in the manufacturing sector and their advantages. Further, it highlights the future of cobots and how they will be a boon for a technology-driven world.

In the paper, a feedback hybrid control including a force feedback control and a position control is proposed to control a four degree of freedom (4-dof) upper limb exoskeleton for supporting human movement at the shoulder, elbow and wrist joints. The novelty of the paper is that it has been able to control all the interaction forces at all links in the exoskeleton robot by using the proposed control. The desired interaction forces at the links and desired position are compared with the measured interaction forces and position, respectively. Then the torque at the shoulder, the torque elbow and the torque wrist joints are controlled to compensate the force error and the position error. The gains of the proposed controller are optimized by using the Balancing Composite Motion Optimization (BCMO). The simulation and control of the 4-dof upper limb exoskeleton using the proposed control is carried out in the paper to show that the interaction forces and the position of the exoskeleton track their desired values.

This paper presents the results of a neural convolutional system for recognizing the wearing of a mask by people entering a building. The algorithm is provided with input data thanks to cameras placed in the humanoid robot COVIDguard. The data collected by the humanoid – the temperature of people entering the facility, the location of the person, the way the protective mask was applied – are stored in the cloud, which enables the application of advanced image recognition algorithms and, consequently, the tracking of people within the range of the robot’s sensory systems by the administrator and the verification of the security level in the given premises. The paper presents the architecture of the intelligent COVIDguard platform, the structure of the sensory system and the results of the neural network learning.