Pada masa kini, robot berautonomi telah digunakan secara meluas dalam pelbagai bidang industri. Teknologi kini yang canggih telah membolehkan rekabentuk robot yg canggih. Ini menjadikan ia suatu sistem yg sangat rumit, dan kebarangkalian berlaku kesalahan sistem juga bertambah. Jika sebarang kesalahan pergerakan robot tidak dikesan, ini boleh menyebabkan berlakunya tragedi yang tidak diingini. Oleh itu, satu sistem yang mempunyai keupayaan pengesanan, pengiktirafan, dan pengelasan kesalahan gerak-geri robot amat diperlukan. Rangkaian Neural Buatan (RNB) boleh digunakan untuk menjalankan tugasan mengesan dan mengelas kesalahan robot. Dalam projek ini, RNB jenis generik dan khusus telah dibangunkan menggunakan RNB Pengajian Kompetitif, untuk pengelasan dua jenis kesalahan dalam pergerakan robot dan pergerakan normal (tiada kesalahan). Prestasi kedua-dua jenis sistem generik dan khusus dibandingkan. Ketepatan yang diperolehi bagi sistem generik ialah 73.85% kelas normal, 69.92% bagi kelas langgaran dan 29.09% bagi kelas halangan. Sistem khusus pula memperolehi ketepatan 92.31% bagi kelas normal, 94.74% bagi kelas kesalahn langgaran dan 96.36% bagi kelas kesalahan halangan. Ini menunjukkan bahawa RNB pengajian kompetitif jenis khusus adalah lebih tepat daripada jenis generik dalam pengelasan kesalahan gerakan robot.
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Nowadays, the autonomous robots are widely used in many kinds of industrial field. The latest technology enables the design of advanced and complex robot. As a result of the increasing of complexity, the probability of the occurring of system faults will increase proportional too. If the faults do not have any proper solutions, these will lead to the calamitous consequences especially for the autonomous robots. Hence, there is a growing need for the system for detection, recognition, classification and find solution in order to handle the fault as soon as it occurs. Artificial Neural Network (ANN) can be used for the detection and classification of the robot faults. In this project, generic and specialized neural systems are constructed using the competitive learning in order to do the classification of two types of faults, i.e. abnormal robot movement and normal (no faults). Performances of both generic and specialized neural systems are compared. The percentage of correctness for generic neural system is Normal class, 73.85%; Collision class, 69.92%; and Obstruction class, 29.09%. For specialized neural system, the percentage of correctness is 92.31%, 94.74%, and 96.36% for Normal, Collision and Obstruction classes, respectively. As a conclusion, specialized neural system has the better percentage of correctness than the generic neural system in the robot fault recognition.
Performance comparison between a generic and specialized artificial neural system for robotic fault recognition_Teo Yu Xiang_E3_2009_875004543_00003095364_NI