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Kostavelis, Ioannis; Kargakos, Andreas; Skartados, Evangelos; Peleka, Georgia; Giakoumis, Dimitrios; Sarantopoulos, Iason; Agriomallos, Ioannis; Doulgeri, Zoe; Endo, Satoshi; Stüber, Heiko; Janjoš, Faris; Hirche, Sandra; Tzovaras, Dimitrios
Robotic Assistance in Medication Intake: A Complete Pipeline
During the last few decades, great research endeavors have been applied to healthcare robots, aiming to develop companions that extend the independent living of elderly people. To deploy such robots into the market, it is expected that certain applications should be addressed with repeatability and robustness. Such application is the assistance with medication-related activity, a common need for the majority of elderly people, referred from here on as medication adherence. This paper presents a novel and complete pipeline for assistance provision in monitoring and serving of medication, using a mobile manipulator embedded with action, perception and cognition skills. The challenges tackled in this work comprise, among others, that the robot locates the medication box placed in challenging spots by applying vision based strategies, thus enabling robust grasping. The grasping is performed with strategies that allow environmental contact, accommodated by the manipulator’ s admittance controller which offers compliance behavior during interaction with the environment. Robot navigation is applied for the medication delivery, which, combined with active vision methods, enables the automatic selection of parking positions, allowing efficient interaction and monitoring of medication intake activity. The robot skills are orchestrated by a partially observable Markov decision process mechanism which is coupled with a task planner. This enables assistance scenario guidance and offers repeatability as well as gentle degradation of the system upon a failure, thus avoiding uncomfortable situations during human– robot interaction. Experiments have been conducted on the full pipeline, including robot’ s deployment in 12 real house environments with real participants that led to very promising results with valuable findings for similar future applications.
medial_endo; hri_endo; ramcip
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Applied Sciences
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