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dc.contributor.authorThoresen, Andreas
dc.date.accessioned2019-08-08T23:47:18Z
dc.date.available2019-08-08T23:47:18Z
dc.date.issued2019
dc.identifier.citationThoresen, Andreas. Artificial Finger Control - Inverse kinematics in soft robotics. Master thesis, University of Oslo, 2019
dc.identifier.urihttp://hdl.handle.net/10852/69048
dc.description.abstractMore compliant robots have certain benefits when coming to cooperating with humans. Standard industrial robots today require many safety measurements to be legal and safe to operate. Soft robots can therefor be safer, have more compliance for external forces naturally. Positioning and control of a robot can be a difficult task and computational heavy. In this thesis, we explore the possibility of controlling a soft artificial finger with heuristic methods such as neural networks. The finger would be compliant and in no way be endangering humans or the environment it is in. Due to the compliance in the materials the servos, cannot be broken as a result of misuse or external forces applied to the finger. Design of the finger will be discussed and reasoned. Inspiration from the human anatomy will be used, but for machine learning purposes there will be simplifications. Datasets will be generated using the model made and a webcam for tracking the fingertip. The collection of data and the impact the data have on the algorithms will be discussed. We will test different dataset with different amounts of joints. Distal supervised learning will be used as an approach to solve the inverse kinematics of the soft finger and see if the neural net can control the finger. Focusing on being accurate enough to position in space, but also learning fast to be able to adapt to external changes, that may affect the finger. The thesis will also implement a smaller regression method for the dataset. KNN-regression will be tested and compared to the result of the distal supervised learning. Both of the methods were able to learn the inverse kinematics of the soft roboteng
dc.language.isoeng
dc.subject
dc.titleArtificial Finger Control - Inverse kinematics in soft roboticseng
dc.typeMaster thesis
dc.date.updated2019-08-09T23:45:52Z
dc.creator.authorThoresen, Andreas
dc.identifier.urnURN:NBN:no-72194
dc.type.documentMasteroppgave
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/69048/1/mymaster.pdf


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