Autocompletion algorithm for simple trajectories
Algoritmus pro automatické doplňovaní jednoduchých trajektorií
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České vysoké učení technické v Praze
Czech Technical University in Prague
Czech Technical University in Prague
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Abstract
Motion planning is one of the core problems that is being studied extensively by robotics researchers in the present day. Among the many techniques available, planning via automatic path/trajectory generation is one of the most widely used approaches. This thesis has implemented a system using tools from computer vision, computer graphics and supervised machine learning which can 'autocomplete' a user demonstrated trajectory segment on differently shaped blocks. This means that the final trajectory generated will be based on the shape of the block with the user's demonstration superimposed on it. The aim is for the trajectory to be utilized in planning the motions of an industrial robot. In the process of developing this system, this thesis provides a comprehensive review of the subject fields utilized and covers the basic intuition behind the algorithms used in the system. The final results of this thesis show that it is possible to automatically generate smooth and continuous trajectories that are non-photorealistic using information from a human made trajectory segment. Although the system is functional, it should be considered as a proof of concept rather than as an industrial level implementation. There is much improvement to be made to this thesis' system before it can be considered fit enough to be deployed in an industrial setting.
Motion planning is one of the core problems that is being studied extensively by robotics researchers in the present day. Among the many techniques available, planning via automatic path/trajectory generation is one of the most widely used approaches. This thesis has implemented a system using tools from computer vision, computer graphics and supervised machine learning which can 'autocomplete' a user demonstrated trajectory segment on differently shaped blocks. This means that the final trajectory generated will be based on the shape of the block with the user's demonstration superimposed on it. The aim is for the trajectory to be utilized in planning the motions of an industrial robot. In the process of developing this system, this thesis provides a comprehensive review of the subject fields utilized and covers the basic intuition behind the algorithms used in the system. The final results of this thesis show that it is possible to automatically generate smooth and continuous trajectories that are non-photorealistic using information from a human made trajectory segment. Although the system is functional, it should be considered as a proof of concept rather than as an industrial level implementation. There is much improvement to be made to this thesis' system before it can be considered fit enough to be deployed in an industrial setting.
Motion planning is one of the core problems that is being studied extensively by robotics researchers in the present day. Among the many techniques available, planning via automatic path/trajectory generation is one of the most widely used approaches. This thesis has implemented a system using tools from computer vision, computer graphics and supervised machine learning which can 'autocomplete' a user demonstrated trajectory segment on differently shaped blocks. This means that the final trajectory generated will be based on the shape of the block with the user's demonstration superimposed on it. The aim is for the trajectory to be utilized in planning the motions of an industrial robot. In the process of developing this system, this thesis provides a comprehensive review of the subject fields utilized and covers the basic intuition behind the algorithms used in the system. The final results of this thesis show that it is possible to automatically generate smooth and continuous trajectories that are non-photorealistic using information from a human made trajectory segment. Although the system is functional, it should be considered as a proof of concept rather than as an industrial level implementation. There is much improvement to be made to this thesis' system before it can be considered fit enough to be deployed in an industrial setting.
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trajecory generation, digital image processing, shape recognition, computer graphics, non-photorealistic rendering, machine learning, trajectory classification, trajecory generation, digital image processing, shape recognition, computer graphics, non-photorealistic rendering, machine learning, trajectory classification