Abstract-Automation of robotic surgery has the potential to improve the performance of surgeons and the quality of the life of patients. However, the automation of surgical tasks has challenging problems that must be resolved. One such problem is adaptive online trajectory planning based on the state of the surrounding dynamic environment. This study presents a framework for online trajectory planning in a dynamic environment for automatic assistance in robotic surgery. In the proposed system, a demonstration under various states of the environment is used for learning. The distribution of the demonstrated trajectory over the environmental conditions is modeled using a statistical model. The trajectory, under given environmental conditions, is computed as a conditional expectation using the learned model. Because of its low computational cost, the proposed scheme is able to generalize and plan a trajectory online in a dynamic environment. To design the motion of the system to track the planned trajectory in a stable and smooth manner, the concept of a sliding mode control was employed; its stability was proved theoretically. The proposed scheme was implemented on a robotic surgical system and the performance was verified through experiments and simulations. These experiments and simulations verified that the developed system successfully planned and updated the trajectories of the learned tasks in response to the changes in the dynamic environment.
Pediatric minimally invasive surgery requires dexterous manipulation of surgical instruments to handle fragile tissues in a small and intricate workspace. Thus, pediatric surgeons need advanced surgical skills in order to avoid complications 1) ;however, junior pediatric surgeons have fewer opportunities to learn and practice these skills due to the limited number of pediatric patients compared to that of adult patients. Regardless of the limited number of pediatric patients, training of pediatric surgeons is very important as pediatric surgical outcomes can have a strong influence on the long-term quality of life of patients. To provide surgical skill assessment and training opportunities , several groups have developed simulators and models to replicate the conditions of pediatric minimally invasive surgery. For example, Azzie et al. proposed a pediatric laparoscopic surgery simulator using a miniature box trainer 2). Ieiri et al. developed a suture ligature model of the diaphragmatic crura in infant fundoplication 3). Barsness et al. developed a three-dimensional(3 D)-printed pediatric chest cavity model for training of pediatric thoracoscopic surgical skills 4). Harada et al. also developed a 3 Dprinted model replicating the chest cavity of a 1-year old patient with a force sensor placed inside its ribcage 5). These groups also conducted surgical skill assessments to show the validity of these simulators and models;the method typically used to demonstrate the validities of surgical simulators was described by Mason et al. 6) and is widely used, as shown in a survey by Ahmed et al. 7). In principle, there are subjective and objective validation methods. The construct validity is an objective validity
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