Publications

Providing force feedback as relevant information in current Robot-Assisted Minimally Invasive Surgery systems constitutes a technological challenge due to the constraints imposed by the surgical environment. In this context, force estimation techniques represent a potential solution, enabling to sense the interaction forces between the surgical instruments and soft-tissues. Specifically, if visual feedback is available for observing soft-tissues’ deformation, this feedback can be used to estimate the forces applied to these tissues. To this end, a force estimation model, based on Convolutional Neural Networks and Long-Short Term Memory networks, is proposed in this work. This model is designed to process both, the spatiotemporal information present in video sequences and the temporal structure of tool data (the surgical tool-tip trajectory and its grasping status). A series of analyses are carried out to reveal the advantages of the proposal and the challenges that remain for real applications. This research work focuses on two surgical task scenarios, referred to as pushing and pulling tissue. For these two scenarios, different input data modalities and their effect on the force estimation quality are investigated. These input data modalities are tool data, video sequences and a combination of both. The results suggest that the force estimation quality is better when both, the tool data and video sequences, are processed by the neural network model. Moreover, this study reveals the need for a loss function, designed to promote the modeling of smooth and sharp details found in force signals. Finally, the results show that the modeling of forces due to pulling tasks is more challenging than for the simplest pushing actions.

JTD Keywords: Convolutional neural networks, Force estimation, LSTM networks, Robotic surgery

One of the challenges of the robotics community is to develop robots that behave more and more autonomously. Therefore, it is necessary to establish new design criteria, as well as more complex methodologies supporting the analysis of associated risks. The procedure described in this paper includes civil liability as an additional criterion to validate the safety of a surgical robot. In order to understand the concept, a methodology is presented through the description of a simple case. This work aims to establish the basis for a further implementation.

JTD Keywords: Design methodology, Product development, Product liability, Safety, Robotic surgery, Cognitive robotics

This paper presents a novel teleoperation paradigm, the Virtual Robot (VR), focused on facilitating the surgeon tasks in minimally invasive robotic surgery. The VR has been conceived to increase the range of applicability of traditional master slave teleoperation architectures by means of an automatic cooperative behavior that assigns the execution of the ongoing task to the most suitable robot. From the user's point of view, the VR internal operation must be automatic and transparent. A set of evaluation indexes have been developed to obtain the suitability of each robot as well as an algorithm to determine the optimal instant of time to execute a task transfer. Several experiments demonstrate the usefulness of the VR, as well as indicates the next steps of the research.

JTD Keywords: Cameras, Collision avoidance, Indexes, Joints, Robots, Surgery, Trajectory, Medical robotics, Surgery, Telerobotics, VR internal operation, Automatic cooperative behavior, Evaluation indexes, Master slave teleoperation architectures, Minimally invasive robotic surgery, Task transfer, Virtual robot