CHAPTER 5
incorporating motion reference model with raw depth data. By recognizing and imitating the worker’s assembly motion for robot teaching, generated robot path contains the knowledge of the worker and consideration of the geometrical constraints for the assembly task.
5.2 Future research
Raw depth data acquired from the depth camera contains the noises and the incorrect data. Therefore, the interface of the raw depth data acquisition requires improvement to minimize the error factor occurred during the observation. By applying pose estimation algorithms such as ICP for generating trajectory of the part, reliability of the acquired trajectory can be improved. The data acquisition systems using multiple depth camera also can improve the reliability of the data. Moreover, multiple depth camera can afford to observe complex part’s movement such as spin, and minimize the shaded area during the assembly task.
Trajectory key points extraction can be conducted by applying various model such as hidden markov model and Bayesian networks. The gaussian mixture model also can be conducted by weighting the trajectories. The five sets of the trajectory which used for key points extraction in this thesis also shows the different tendency according to certain section. By weighting the data according to its importance, reliability of the key points will be improved.
The classification of the activities during the assembly process is conducted using distance between the reference points. This approach has many ambiguities in classifying activities. Therefore, more general and precise classification of the activities is possible by improving and applying methods such as described in the literature review in this thesis.
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