Pembangunan kerangka konsep penyertaan latihan : kajian kes staf akademik di universiti awam Malaysia

Latihan merupakan aktiviti formal dan usaha berterusan yang dilakukan oleh pengurusan universiti dalam meningkatkan prestasi dan kualiti kerja staf akademik di Universiti Awam Malaysia. Latihan juga penting kepada staf akademik meningkatkan keupayaan dan keyakinan dalam menjalankan tugas yang diberikan. Namun terdapat isu dan permasalahan tentang ketidakcukupan penyertaan staf akademik dalam latihan. Kajian lepas menunjukkan kebanyakkan permasalahan dalam penyertaan latihan diselesaikan hanya melalui kajian peringkat penentuan faktor sahaja. Oleh itu, kajian ini dijalankan bagi meneroka dan mendalami faktor-faktor yang mempengaruhi penyertaan staf akademik Universiti Awam Malaysia dalam latihan. Seterusnya satu kerangka konsep baharu telah dibangunkan bagi melihat signifikan perkaitan faktor yang mempengaruhi penyertaan dalam latihan. Kajian ini telah menggunakan kaedah kualitatif kajian kes di mana seramai tujuh (7) orang pegawai latihan dan dua puluh lapan (28) orang staf akademik Universiti Awam Malaysia telah ditemu bual. Temu bual separa berstruktur bersama kaedah ’probing’ digunakan bagi mendapatkan maklumat dengan lebih mendalam. Data yang diperolehi telah dianalisis dengan menggunakan kaedah Analisis Tematik. Berdasarkan analisa yang dijalankan, didapati empat faktor domain mempengaruhi penyertaan staf akademik Universiti Awam Malaysia dalam latihan. Empat faktor yang dimaksudkan adalah faktor kerja, individu, organisasi dan latihan. Berdasarkan keempat-empat faktor yang telah dikenalpasti, kajian ini telah membangunkan kerangka konsep berdasarkan pengintegrasian teori (Teori Tindakan Bersebab, Teori Tingkahlaku Terancang, Teori Hierarki Keperluan Maslow, Teori Jangkaan, Teori Pembelajaran Sosial, Teori Pembelajaran Dewasa, Teori Modal Insan, Teori Matlamat dan Teori Kerjaya Holland) yang berkaitan penyertaan dalam latihan. Kerangka konsep yang dicadangkan dapat mengisi kelompongan permasalahan kajian serta boleh dimanfaatkan oleh pihak yang berkepentingan terutama Universiti Awam Malaysia dan Kementerian Pendidikan Tinggi Malaysia (KPTM) yang merupakan badan induk kepada Institusi Pengajian Tinggi Malaysia bagi meningkatkan penyertaan staf akademik dalam latihan

Active compliance control strategies for multifingered robot hand

Safety issues have to be enhanced when the robot hand is grasping objects of different shapes, sizes and stiffness. The inability to control the grasping force and finger stiffness can lead to unsafe grasping environment. Although many researches have been conducted to resolve the grasping issues, particularly for the object with different shape, size and stiffness, the grasping control still requires further improvement. Hence, the primary aim of this work is to assess and improve the safety of the robot hand. One of the methods that allows a safe grasping is by employing an active compliance control via the force and impedance control. The implementation of force control considers the proportional–integral–derivative (PID) controller. Meanwhile, the implementation of impedance control employs the integral slidingmode controller (ISMC) and adaptive controller. A series of experiments and simulations is used to demonstrate the fundamental principles of robot grasping. Objects with different shape, size and stiffness are tested using a 3-Finger Adaptive Robot Gripper. The work introduces the Modbus remote terminal unit [RTU] protocol, a low-cost force sensor and the Arduino IO Package for a real-time hardware setup. It is found that, the results of the force control via PID controller are feasible to maintain the grasped object at certain positions, depending on the desired grasping force (i.e., 1N and 8N). Meanwhile, the implementation of impedance control via ISMC and adaptive controller yields multiple stiffness levels for the robot fingers and able to reduce collision between the fingers and the object. However, it was found that the adaptive controller produces better impedance control results as compared to the ISMC, with a 33% efficiency improvement. This work lays important foundations for long-term related research, particularly in the field of active compliance control that can be beneficial to human–robot interaction (HRI)

Learning-based robotic manipulation for dynamic object handling : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Mechatronic Engineering at the School of Food and Advanced Technology, Massey University, Turitea Campus, Palmerston North, New Zealand

Figures are re-used in this thesis with permission of their respective publishers or under a Creative Commons licence.Recent trends have shown that the lifecycles and production volumes of modern products are shortening. Consequently, many manufacturers subject to frequent change prefer flexible and reconfigurable production systems. Such schemes are often achieved by means of manual assembly, as conventional automated systems are perceived as lacking flexibility. Production lines that incorporate human workers are particularly common within consumer electronics and small appliances. Artificial intelligence (AI) is a possible avenue to achieve smart robotic automation in this context. In this research it is argued that a robust, autonomous object handling process plays a crucial role in future manufacturing systems that incorporate robotics—key to further closing the gap between manual and fully automated production. Novel object grasping is a difficult task, confounded by many factors including object geometry, weight distribution, friction coefficients and deformation characteristics. Sensing and actuation accuracy can also significantly impact manipulation quality. Another challenge is understanding the relationship between these factors, a specific grasping strategy, the robotic arm and the employed end-effector. Manipulation has been a central research topic within robotics for many years. Some works focus on design, i.e. specifying a gripper-object interface such that the effects of imprecise gripper placement and other confounding control-related factors are mitigated. Many universal robotic gripper designs have been considered, including 3-fingered gripper designs, anthropomorphic grippers, granular jamming end-effectors and underactuated mechanisms. While such approaches have maintained some interest, contemporary works predominantly utilise machine learning in conjunction with imaging technologies and generic force-closure end-effectors. Neural networks that utilise supervised and unsupervised learning schemes with an RGB or RGB-D input make up the bulk of publications within this field. Though many solutions have been studied, automatically generating a robust grasp configuration for objects not known a priori, remains an open-ended problem. An element of this issue relates to a lack of objective performance metrics to quantify the effectiveness of a solution—which has traditionally driven the direction of community focus by highlighting gaps in the state-of-the-art. This research employs monocular vision and deep learning to generate—and select from—a set of hypothesis grasps. A significant portion of this research relates to the process by which a final grasp is selected. Grasp synthesis is achieved by sampling the workspace using convolutional neural networks trained to recognise prospective grasp areas. Each potential pose is evaluated by the proposed method in conjunction with other input modalities—such as load-cells and an alternate perspective. To overcome human bias and build upon traditional metrics, scores are established to objectively quantify the quality of an executed grasp trial. Learning frameworks that aim to maximise for these scores are employed in the selection process to improve performance. The proposed methodology and associated metrics are empirically evaluated. A physical prototype system was constructed, employing a Dobot Magician robotic manipulator, vision enclosure, imaging system, conveyor, sensing unit and control system. Over 4,000 trials were conducted utilising 100 objects. Experimentation showed that robotic manipulation quality could be improved by 10.3% when selecting to optimise for the proposed metrics—quantified by a metric related to translational error. Trials further demonstrated a grasp success rate of 99.3% for known objects and 98.9% for objects for which a priori information is unavailable. For unknown objects, this equated to an improvement of approximately 10% relative to other similar methodologies in literature. A 5.3% reduction in grasp rate was observed when removing the metrics as selection criteria for the prototype system. The system operated at approximately 1 Hz when contemporary hardware was employed. Experimentation demonstrated that selecting a grasp pose based on the proposed metrics improved grasp rates by up to 4.6% for known objects and 2.5% for unknown objects—compared to selecting for grasp rate alone. This project was sponsored by the Richard and Mary Earle Technology Trust, the Ken and Elizabeth Powell Bursary and the Massey University Foundation. Without the financial support provided by these entities, it would not have been possible to construct the physical robotic system used for testing and experimentation. This research adds to the field of robotic manipulation, contributing to topics on grasp-induced error analysis, post-grasp error minimisation, grasp synthesis framework design and general grasp synthesis. Three journal publications and one IEEE Xplore paper have been published as a result of this research