Implementation of multi-algorithm controllers for path determination in mobile robot systems

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dc.contributor.advisorFernandez, Benito R.
dc.creatorHitijahubessy, Adrianus Victoren
dc.date.accessioned2015-05-06T17:00:41Zen
dc.date.accessioned2018-01-22T22:27:46Z
dc.date.available2015-05-06T17:00:41Zen
dc.date.available2018-01-22T22:27:46Z
dc.date.issued2006-05en
dc.descriptiontexten
dc.description.abstractRecent advancements in control systems, such as the ones used in missile technology in the military or autonomous vehicle development have motivated this study in an attempt to explore various control algorithms and their implementation relevant those applications. Both missile interceptor and autonomous vehicle technology require precise and responsive control system to accurately determine the projectile path of pursuer to strike a moving target or reach a static finish line.The objective of this study is to investigate the performance of several control techniques for a mobile robot to autonomously track and pursue a moving object. Computer model is developed to numerically predict the path taken by the pursuer as it tracks an object moving in regular or random manner. In the computer simulation, the robot's path is calculated using three different techniques: reactive controller, linear estimation, and artificial neural network. Fitness of each method may be determined by evaluating the controller against several factors, such as interception time, steady-state positional error, steady-state time (settling time) and algorithm complexity, listed in decreasing order of importance. A working experimental model is developed to validate the controller selection determined from the computer model simulation. In the experimental setting, the primary inputs to the robot are visual images from cameras. The experiments are carried out with the robot receiving visual inputs from two different perspectives, overhead and frontal vision. Robust image processing technique becomes a topic of significant importance for the system. To manipulate visual images in real-time from raw inputs to comprehensible data, while maintaining fast computational time is a challenge that is addressed in this study. The results from computer simulations show that artificial neural network is a more powerful control algorithm, capable of estimating the object's path more accurately than the other two controllers, resulting in smaller steady-state positional error. The experimental results confirm this conclusion as artificial neural network outperforms the reactive and linear controller by intercepting the object more quickly, i.e. shorter interception time.en
dc.description.departmentMechanical Engineeringen
dc.format.mediumelectronicen
dc.identifier.urihttp://hdl.handle.net/2152/29704en
dc.language.isoengen
dc.rightsCopyright is held by the author. Presentation of this material on the Libraries' web site by University Libraries, The University of Texas at Austin was made possible under a limited license grant from the author who has retained all copyrights in the works.en
dc.rights.restrictionRestricteden
dc.subjectRoboten
dc.subjectControl systemsen
dc.subjectComputer modelen
dc.titleImplementation of multi-algorithm controllers for path determination in mobile robot systemsen
dc.typeThesisen

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