Semi-Autonomous Robotic Surgery for Space Exploration Missions

摘要

Space exploration missions to the moon and Mars cannot count on having a surgeon physically present to perform surgical procedures. Furthermore, telesurgery is not feasible at these distances due to network latency ranging from 3 seconds to several minutes. To address this need, a robotic surgical system can be trained by expert surgeons prospectively so that a health-preserving or life-saving procedure can be performed semi-autonomously when needed. The approach of path generation using artificial neural networks allows for an effective and scalable solution for the supervised learning and real-time performance of a surgical procedure. This study makes use of long short-term memory (LSTM) recurrent neural networks (RNNs) in conjunction with Evolino or back propagation learning algorithms for end-effector path optimization. The RNN-generated path is trained from human-performed procedures in a simulated environment. Changes in movement of path markers are accounted for by adjusting the end-effector acceleration with respect to target markers along the path. Results include smooth generated paths successfully meeting the defined procedure requirements of accuracy and speed in both static and dynamic environments.