DESERTS: DElay-tolerant SEmi-autonomous Robot Teleoperation for Surgery

Telesurgery can be hindered by high-latency and low-bandwidth communication networks, often found in austere settings. Even delays of less than one second are known to negatively impact surgeries. To tackle the effects of connectivity associated with telerobotic surgeries, we propose the DESERTS framework. DESERTS provides a novel simulator interface where the surgeon can operate directly on a virtualized reality simulation and the activities are mirrored in a remote robot, almost simultaneously. Thus, the surgeon can perform the surgery uninterrupted, while high-level commands are extracted from his motions and are sent to a remote robotic agent. The simulated setup mirrors the remote environment, including an alphablended view of the remote scene. The framework abstracts the actions into atomic surgical maneuvers (surgemes) which eliminate the need to transmit compressed video information. This system uses a deep learning based architecture to perform live recognition of the surgemes executed by the operator. The robot then executes the received surgemes, thereby achieving semi-autonomy. The framework's performance was tested on a peg transfer task. We evaluated the accuracy of the recognition and execution module independently as well as during live execution. Furthermore, we assessed the framework's performance in the presence of increasing delays. Notably, the system maintained a task success rate of 87% from no-delays to 5 seconds of delay.

Automated summary

The DESERTS framework tackles the challenges of high-latency communication in telesurgery by providing a simulator interface and utilizing deep learning architecture. It ensures accurate surgical operations and efficient execution by the remote robot.