Three-dimensional (3D) imaging and infrared (IR) thermography are powerful tools in many areas in engineering and sciences. Their joint use is of great interest in the buildings sector, allowing inspection and non-destructive testing of elements as well as an evaluation of the energy efficiency. When dealing with large and complex structures, as buildings (particularly historical) generally are, 3D thermography inspection is enhanced by Unmanned Aerial Vehicles (UAV—also known as drones). The aim of this paper is to propose a simple and cost-effective system for aerial 3D thermography of buildings. Special attention is thus payed to instrument and reconstruction software choice. After a very brief introduction to IR thermography for buildings and 3D thermography, the system is described. Some experimental results are given to validate the proposal.

Autonomy is the next frontier of research in robotic surgery and its aim is to improve the quality of surgical procedures in the next future. One fundamental requirement for autonomy is advanced perception capability through vision sensors. In this paper, we propose a novel calibration technique for a surgical scenario with a da Vinci Research Kit (dVRK) robot. Camera and robotic arms calibration are necessary to precise position and emulate expert surgeon. The novel calibration technique is tailored for RGB-D cameras. Different tests performed on relevant use cases prove that we significantly improve precision and accuracy with respect to state of the art solutions for similar devices on a surgical-size setups. Moreover, our calibration method can be easily extended to standard surgical endoscope used in real surgical scenario.

In this paper we propose a new methodology to model surgical procedures that is specifically tailored to semi-autonomous robotic surgery. We propose to use a restricted version of statecharts to merge the bottom-up approach, based on data-driven techniques (e.g., machine learning), with the top-down approach based on knowledge representation techniques. We consider medical knowledge about the procedure and sensing of the environment in two concurrent regions of the statecharts to facilitate re-usability and adaptability of the modules. Our approach allows producing a well defined procedural model exploiting the hierarchy capability of the statecharts, while machine learning modules act as soft sensors to trigger state transitions. Integrating data driven and prior knowledge techniques provides a robust, modular, flexible and re-configurable methodology to define a surgical procedure which is comprehensible by both humans and machines. We validate our approach on the three surgical phases of a Robot-Assisted Radical Prostatectomy (RARP) that directly involve the assistant surgeon: bladder mobilization, bladder neck transection, and vesicourethral anastomosis, all performed on synthetic manikins.

In this article, we propose a novel bilateral teleoperation architecture for a multiarms system based on the two-layer approach. Exploiting the concept of shared energy tank, a passivity layer guarantees the passivity of the overall architecture with respect to destabilizing factors such as time delays in the communication channel. The desired behavior can then be freely designed in the transparency layer. The formulation of the energy tank is first revised, allowing a more efficient use of energy, and then extended, allowing explicitly the use of both admittance and impedance causality robots. A novel framework capable of combining the use of teleoperated and autonomous robots is proposed. The architecture has been tested and validated on a multiarms system in a realistic surgical scenario with the da Vinci research kit (dVRK) and an autonomous arm holding the endoscope.

Bilateral teleoperation systems are often used in safety-critical scenarios where human operators may interact with the environment remotely, as in robotic-assisted surgery or nuclear plant maintenance. Teleoperation's stability and transparency are the two most important properties to be satisfied, but they cannot be optimized independently since they are in contrast. This paper presents a passive linear MPC control scheme to implement bilateral teleoperation that optimizes the trade-off between stability and transparency (a.k.a. performance). First, we introduce a linear virtual energy tank with a novel energy-sharing policy, allowing us to define a passive linear MPC. Second, we provide conditions to guarantee the stability of the non-linear closed-loop system. We validate the proposed approach in a teleoperation scheme using two 7-DOF manipulators while performing an assembly task. This novel passivity-based bilateral teleoperation using linear MPC and linearized energy tank reduces the computational effort of existing passive non-linear MPC controllers.

Vitreoretinal surgery includes a group of highly complex microsurgical procedures that demand precision. Robotic systems can enhance surgical performance, particularly for novice surgeons, while ensuring patient safety through advanced sensing capabilities. Optical Coherence Tomography (OCT), commonly used for eye anatomy imaging, is typically implemented via microscopes or diagnostic devices. This paper introduces the GEYEDANCE system, a bilateral teleoperated microsurgery platform integrating OCT directly at the end-effector of its remote manipulator, offering multi-modal feedback. The system enables intraoperative global eye modelling and surface reconstruction by exploiting a neural network-based tool-to-tissue distance estimation module. Its performance was validated in the operating room using ex-vivo eyes, effectively simulating the surgical steps of various vitreoretinal procedures.