Background
We demonstrate the first self-learning, context-sensitive, autonomous camera-guiding robot applicable to minimally invasive surgery. The majority of surgical robots nowadays are telemanipulators without autonomous capabilities. Autonomous systems have been developed for laparoscopic camera guidance, however following simple rules and not adapting their behavior to specific tasks, procedures, or surgeons.
Methods
The herein presented methodology allows different robot kinematics to perceive their environment, interpret it according to a knowledge base and perform context-aware actions. For training, twenty operations were conducted with human camera guidance by a single surgeon. Subsequently, we experimentally evaluated the cognitive robotic camera control. A VIKY EP system and a KUKA LWR 4 robot were trained on data from manual camera guidance after completion of the surgeon’s learning curve. Second, only data from VIKY EP were used to train the LWR and finally data from training with the LWR were used to re-train the LWR.
Results
The duration of each operation decreased with the robot’s increasing experience from 1704 s ± 244 s to 1406 s ± 112 s, and 1197 s. Camera guidance quality (good/neutral/poor) improved from 38.6/53.4/7.9 to 49.4/46.3/4.1% and 56.2/41.0/2.8%.
Conclusions
The cognitive camera robot improved its performance with experience, laying the foundation for a new generation of cognitive surgical robots that adapt to a surgeon’s needs.
Up to date, medical robots for minimal invasive surgery do not provide assistance appropriate to the workflow of the intervention. A simple concept of a cognitive system is presented, which is derived from a classic closed-loop control. As implementation, we present a cognitive medical robot system using lightweight robots with redundant kinematics. The robot system includes several control modes and human-machine interfaces. We focus on describing knowledge acquisition about the workflow of an intervention and present two example applications utilizing the acquired knowledge: autonomous camera guidance and planning of minimal invasive port (trocar) positions in combination with an initial robot setup. Port planning is described as optimization problem. The autonomous camera system includes a mid-term movement prediction of the ongoing intervention. The cognitive approach to a medical robot system includes taking the environment into account. The goal is to create a system that acts as a human assistant, who perceives the situation, understands the context based on his knowledge and acts appropriate.
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