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Search: WFRF:(Alhusin Alkhdur Abdullah 1980 )

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1.
  • Alhusin Alkhdur, Abdullah, 1980-, et al. (author)
  • Advancing Assembly Through Human-Robot Collaboration : Framework and Implementation
  • 2020
  • In: Reinventing mechatronics. - Cham : Springer Nature. ; , s. 111-126
  • Conference paper (peer-reviewed)abstract
    • The chapter presents a framework for establishing human-robot collaborative assembly in industrial environments. To achieve this, the chapter first reviews the subject state of the art and then addresses the challenges facing researchers. The chapter provides two examples of human-robot collaboration. The first is a scenario where a human is remotely connected to an industrial robot, and the second is where a human collaborates locally with a robot on a shop floor. The chapter focuses on the human-robot collaborative assembly of mechanical components, both on-site and remotely. It also addresses sustainability issues from the societal perspective. The main research objective is to develop safe and operator-friendly solutions for human-robot collaborative assembly within a dynamic factory environment. The presented framework is evaluated using defined scenarios of distant and local assembly operations when the experimental results show that the approach is capable of effectively performing human-robot collaborative assembly tasks.
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2.
  • Alhusin Alkhdur, Abdullah, 1980-, et al. (author)
  • Intelligent human-robot assembly enabled by brain EEG
  • 2021
  • In: Advanced Human-Robot Collaboration in Manufacturing. - Cham : Springer Nature. ; , s. 351-371
  • Book chapter (other academic/artistic)abstract
    • This chapter reports a framework that can facilitate the interactions between a human's EEG (electroencephalography) signals and an industrial robot. This can be achieved by using an EEG headset that captures the brain signals of the human and send it via Bluetooth to a local workstation for signal processing, feature extraction and classification. The system developed provides the ability for a shop-floor operator to control the robot using own brain signals. The system can cooperate with other channels of communications (gesture, voice, etc.) to strengthen the collaboration between the human and the robot during shared assembly operations. Such a collaboration aims to fuse the high accuracy of the robot with the high versatility of the human. Therefore, the aim is to exploit the strength of both sides and enhance the quality and adaptability of human-robot collaborative assembly operations. This approach is applicable to other types of robots as well, for example ones used for assisting people with severe disability.
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3.
  • Alhusin Alkhdur, Abdullah, 1980- (author)
  • Toward a Sustainable Human-Robot Collaborative Production Environment
  • 2017
  • Doctoral thesis (other academic/artistic)abstract
    • This PhD study aimed to address the sustainability issues of the robotic systems from the environmental and social aspects. During the research, three approaches were developed: the first one an online programming-free model-driven system that utilises web-based distributed human-robot collaboration architecture to perform distant assembly operations. It uses a robot-mounted camera to capture the silhouettes of the components from different angles. Then the system analyses those silhouettes and constructs the corresponding 3D models.Using the 3D models together with the model of a robotic assembly cell, the system guides a distant human operator to assemble the real components in the actual robotic cell. To satisfy the safety aspect of the human-robot collaboration, a second approach has been developed for effective online collision avoidance in an augmented environment, where virtual three-dimensional (3D) models of robots and real images of human operators from depth cameras are used for monitoring and collision detection. A prototype system is developed and linked to industrial robot controllers for adaptive robot control, without the need of programming by the operators. The result of collision detection reveals four safety strategies: the system can alert an operator, stop a robot, move away the robot, or modify the robot’s trajectory away from an approaching operator. These strategies can be activated based on the operator’s location with respect to the robot. The case study of the research further discusses the possibility of implementing the developed method in realistic applications, for example, collaboration between robots and humans in an assembly line.To tackle the energy aspect of the sustainability for the human-robot production environment, a third approach has been developed which aims to minimise the robot energy consumption during assembly. Given a trajectory and based on the inverse kinematics and dynamics of a robot, a set of attainable configurations for the robot can be determined, perused by calculating the suitable forces and torques on the joints and links of the robot. The energy consumption is then calculated for each configuration and based on the assigned trajectory. The ones with the lowest energy consumption are selected.
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4.
  • Alhusin Alkhdur Mohammed, Abdullah, 1980-, et al. (author)
  • Advanced human-robot collaborative assembly using electroencephalogram signals of human brains
  • 2020
  • In: Procedia CIRP. - : Elsevier B.V.. - 2212-8271. ; , s. 1200-1205
  • Conference paper (peer-reviewed)abstract
    • This paper introduces an intelligent system that can manipulate an industrial robot using the electroencephalogram signals of human brains to perform collaborative assembly tasks. The system is initiated by capturing the brain signals using a wearable headset, and the signals are then filtered to remove any possible artifact. Consequently, the process continues by identifying the brain signals patterns using a classifier based on pre-recorded samples. The classifier's output determines the proper matching of the robot command that is intended by the human. To validate the results, an industrial collaborative assembly scenario of a car manifold is examined as a case study. 
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