Browsing by Author "Onunka, Chiemela."
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Item Autonomous sea craft for search and rescue operations : marine vehicle modelling and analysis.(2011) Onunka, Chiemela.; Bright, Glen.; Stopforth, Riaan.Marine search and rescue activities have been plagued with the problem of risking the lives of rescuers in rescue operations. With increasing developments in sensor technologies, it became a necessity in the marine search and rescue community to develop an autonomous marine craft to assist in rescue operations. Autonomy of marine craft requires a robust localization technique and process. To apply robust localization to marine craft, GPS technology was used to determine the position of the marine craft at any given point in time. Given that the operational environment of the marine was at open air, river, sea etc. GPS signal was always available to the marine craft as there are no obstructions to GPS signal. Adequate cognizance of the current position and states of an unmanned marine craft was a critical requirement for navigation of an unmanned surface vehicle (USV). The unmanned surface vehicle uses GPS in conjunction with state estimated solution provided by inertial sensors. In the absence of the GPS signal, navigation is resumed with a digital compass and inertial sensors to such a time when the GPS signal becomes accessible. GPS based navigation can be used for an unmanned marine craft with the mathematical modelling of the craft meeting the functional requirements of an unmanned marine craft. A low cost GPS unit was used in conjunction with a low cost inertial measurement unit (IMU) with sonar for obstacle detection. The use of sonar in navigation algorithm of marine craft was aimed at surveillance of the operational environment of the marine craft to detect obstacles on its path of motion. Inertial sensors were used to determine the attitude of the marine craft in motion.Item EEG artefact identification and extraction in autonomic wireless network for future coordination and control of semi-autonomous systems.(2015) Onunka, Chiemela.; Bright, Glen.Electroencephalographic signals is used to show correlations between specific forms of cognitive activities and robotic hand motion. This research presents EEG artefact identification, extraction and classification for use in the development of a robotic hand. The findings from the study were used to control a robotic arm and develop a suitable communication network that has no dependence on the human nervous system communication pathways. The research was focused at modelling bio-sensing and bio-monitoring feedback system using electroencephalographic (EEG) as the source signal. An EEG communication system was developed for implementation on the robotic hand developed by the Mechatronics and Robotics Research Group (MR2G). Neuronal activities produce electrical signals on surface of scalp in human beings. EEG the raw material for robot command development was generated from the neuronal activities. Specific techniques were used in modelling the EEG analysis system for implementation on the robotic hand. The techniques used include the Radial Basis Function (RBF) neural network, Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Wavelet Packet Transform (WPT), Multilayer Perceptron Neural Network (MLPNN), Learning Vector Quantization (LVQ) neural network, Bayesian and probabilistic paradigms in developing the EEG artefact identification, extraction and classification model. These techniques were investigated and implemented in order to have an efficient EEG artefact identification and extraction system for controlling the robotic hand. The main contribution of the research was the identification, extraction and classification of electroencephalographic (EEG) artefacts in controlling a robotic hand. The specific contribution made in the research included the development of augmented EEG signal and EEG artefact extraction process using mathematical models. The models were used to develop integrated coordination and control architecture for the robotic arm. The research also made significant contribution to the development of modular Brain-Computer Interface (BCI) communication network. The BCI was augmented in autonomic wireless neural network activated by various EEG artefacts. The robotic hand control command codes were developed and they were modular in their application strategy. This was consolidated with adequate software and hardware architecture which were reconfigurable and leveraged using neuro-symbolic behaviour language in controlling the robotic hand developed by the Mechatronic and Robotic Research Group (MR2G).Item The impact of disruptive technology on the manufacturing process, and productivity, in an advanced manufacturing environment.(2022) Salawu, Ganiyat Abiodun.; Bright, Glen.; Onunka, Chiemela.Disruptive technology plays a critical role in the performance of mechatronic systems in an advanced manufacturing environment. Robots were used to perform pick and place task in a virtual manufacturing environment. Newton-Raphson model, renewal theorem and queuing theory were used to model the disruptive technology and develop decision-making algorithms in an advanced process. The motion of the conveyor belt system starved modeled and simulated to determine suitable design parameters that were compatible with the tasks of the pick and place robot. MATLAB and Engineering Equation Solver (EES) were used to determine static solutions and simulated solutions to the pick and place problem in the advanced manufacturing process. The results from the simulations were used to develop suitable task-dependent operational conditions in the advanced manufacturing environment. The simulation results were used to determine the optimal conveyor speeds required for the robotic tasks. Comparing the throughput rate of the developed system with the simulated system indicated that optimal productivity was achieved when the decision-making algorithms were implemented at the early stages of the manufacturing process.