Comparative analysis of model-based predictive shared control for delayed operation in object reaching and recognition tasks with tactile sensing

Communication delay represents a fundamental challenge in telerobotics: on one hand, it compromises the stability of teleoperated robots, on the other hand, it decreases the user’s awareness of the designated task. In scientific literature, such a problem has been addressed both with statistical models and neural networks (NN) to perform sensor prediction, while keeping the user in full control of the robot’s motion. We propose shared control as a tool to compensate and mitigate the effects of communication delay. Shared control has been proven to enhance precision and speed in reaching and manipulation tasks, especially in the medical and surgical fields. We analyse the effects of added delay and propose a unilateral teleoperated leader-follower architecture that both implements a predictive system and shared control, in a 1-dimensional reaching and recognition task with haptic sensing. We propose four different control modalities of increasing autonomy: non-predictive human control (HC), predictive human control (PHC), (shared) predictive human-robot control (PHRC), and predictive robot control (PRC). When analyzing how the added delay affects the subjects’ performance, the results show that the HC is very sensitive to the delay: users are not able to stop at the desired position and trajectories exhibit wide oscillations. The degree of autonomy introduced is shown to be effective in decreasing the total time requested to accomplish the task. Furthermore, we provide a deep analysis of environmental interaction forces and performed trajectories. Overall, the shared control modality, PHRC, represents a good trade-off, having peak performance in accuracy and task time, a good reaching speed, and a moderate contact with the object of interest

ROBOTIC TELESURGERY: AN INVESTIGATION OF UTILITY, HUMAN ADAPTATION, AND PERFORMANCE

Robotic surgery is a powerful, new method for performing minimally invasive surgery (MIS). The method allows complex procedures through incisions which are 10 mm or less. Robotic surgery has grown rapidly because small MIS incisions result in rapid patient recovery compared to conventional methods. Although surgical robots have the potential of long distance control, insufficient data is available to determine whether long distance robotic surgery, or telesurgery, is practical. Telesurgery could provide multiple benefits, including dissemination of expertise, widespread patient care, cost savings, and improved community care. We describe a series of experiments to investigate telesurgery using a one of a kind telesurgery platform and ground- and satellite-based Internet networks. The networks provided the redundancy and quality of service that would be required for human surgery. Tolerances for performing surgical tasks over a long distance were unknown. We show that operators using the platform can complete dry lab manoeuvres with communication latencies up to 500 ms, with no appreciable increase in error rates. Such latency would be equivalent to a North American transcontinental distance, implying a wide range of telesurgical capability. The characteristics of ground- and satellite-based Internet networks for telesurgery were unavailable. We demonstrate that emulated surgery in animals can be effectively performed using either ground or satellite. The networks can reliably support surgery, and satellite-based surgery can be performed even though latency exceeds 500 ms. Further, satellite bandwidth should be above 5 Mb/s for telesurgery applications. Satellite networks could be used either for back up or primarily where a community does not have ground-based equipment. iii Methods of training operators for telesurgery had not been explored. We demonstrate two methods of training for telesurgery. Operators doing dry lab surgical manoeuvres performed equally well either with sequentially increasing latency or with full latency only, suggesting that both methods of training may be effective. Telesurgery can become a practical method of treatment. Within a few years, more widespread platforms and telecommunications may exist to launch everyday telesurgery procedures

Towards retrieving force feedback in robotic-assisted surgery: a supervised neuro-recurrent-vision approach

Robotic-assisted minimally invasive surgeries have gained a lot of popularity over conventional procedures as they offer many benefits to both surgeons and patients. Nonetheless, they still suffer from some limitations that affect their outcome. One of them is the lack of force feedback which restricts the surgeon's sense of touch and might reduce precision during a procedure. To overcome this limitation, we propose a novel force estimation approach that combines a vision based solution with supervised learning to estimate the applied force and provide the surgeon with a suitable representation of it. The proposed solution starts with extracting the geometry of motion of the heart's surface by minimizing an energy functional to recover its 3D deformable structure. A deep network, based on a LSTM-RNN architecture, is then used to learn the relationship between the extracted visual-geometric information and the applied force, and to find accurate mapping between the two. Our proposed force estimation solution avoids the drawbacks usually associated with force sensing devices, such as biocompatibility and integration issues. We evaluate our approach on phantom and realistic tissues in which we report an average root-mean square error of 0.02 N.Peer ReviewedPostprint (author's final draft

Robot Autonomy for Surgery

Autonomous surgery involves having surgical tasks performed by a robot operating under its own will, with partial or no human involvement. There are several important advantages of automation in surgery, which include increasing precision of care due to sub-millimeter robot control, real-time utilization of biosignals for interventional care, improvements to surgical efficiency and execution, and computer-aided guidance under various medical imaging and sensing modalities. While these methods may displace some tasks of surgical teams and individual surgeons, they also present new capabilities in interventions that are too difficult or go beyond the skills of a human. In this chapter, we provide an overview of robot autonomy in commercial use and in research, and present some of the challenges faced in developing autonomous surgical robots

Stable Hybrid Fuzzy Controller-based Architecture for Robotic Telesurgery Systems

Robotic surgery and remotely controlled teleoperational systems are on the rise. However, serious limitations arise on both the hardware and software side when traditional modeling and control approaches are taken. These limitations include the incomplete modeling of robot dynamics, tool–tissue interaction, human– machine interfaces and the communication channel. Furthermore, the inherent latency of long-distance signal transmission may endanger the stability of a robot controller. All of these factors contribute to the very limited deployment of real robotic telesurgery. This paper describes a stable hybrid fuzzy controller-based architecture that is capable of handling the basic challenges. The aim is to establish high fidelity telepresence systems for medical applications by easily handled modern control solution