Currently, surgeons in operating rooms are forced to focus their attention both on the patient’s body and on flat low-quality surgical monitors, in order to get all the information needed to successfully complete surgeries. The way the data are displayed leads to disturbances of the surgeon’s visuals, which may affect his performances, besides the fact that other members of the surgical team do not have proper visual tools able to aid him. The idea underlying this paper is to exploit mixed reality to support surgeons during surgical procedures. In particular, the proposed experimental setup, employed in the operating room, is based on an architecture that put together the Microsoft HoloLens, a Digital Imaging and Communications in Medicine (DICOM) player and a mixed reality visualization tool (i.e., Spectator View) developed by using the Mixed Reality Toolkit in Unity with Windows 10 SDK. The suggested approach enables visual information on the patient’s body as well as information on the results of medical screenings to be visualized on the surgeon’s headsets. Additionally, the architecture enables any data and details to be shared by the team members or by external users during surgical operations. The paper analyses in detail advantages and drawbacks that the surgeons have found when they wore the Microsoft HoloLens headset during all the ten open abdomen surgeries conducted at the IRCCS Hospital “Giovanni Paolo II” in the city of Bari (Italy). A survey based on Likert scale demonstrates how the use of the suggested tools can increase the execution speed by allowing multitasking procedures, i.e., by checking medical images at high resolution without leaving the operating table and the patient. On the other hand, the survey also reveals an increase in the physical stress and reduced comfort due to the weight of the Microsoft HoloLens device, along with drawbacks due to the battery autonomy. Additionally, the survey seems to encourage the use of DICOM Viewer and Spectator View both for surgical education and for improving surgery outcomes. Note that the real use of the conceived platform in the operating room represents a remarkable feature of this paper, since most if not all the studies conducted so far in literature exploit mixed reality only in simulated environments and not in real operating rooms. In conclusion, the study clearly highlights that, despite the challenges required in the forthcoming years to improve the current technology, mixed reality represents a promising technique that will soon enter the operating rooms to support surgeons during surgical procedures in many hospitals across the world.

In this paper, a novel approach for online terrain characterisation is presented using a skid-steer vehicle. In the context of this research, terrain characterisation refers to the estimation of physical parameters that affects the terrain ability to support vehicular motion. These parameters are inferred from the modelling of the kinematic and dynamic behaviour of a skid-steer vehicle that reveals the underlying relationships governing the vehicle-terrain interaction. The concept of slip track is introduced as a measure of the slippage experienced by the vehicle during turning motion. The proposed terrain estimation system includes common onboard sensors, that is, wheel encoders, electrical current sensors and yaw rate gyroscope. Using these components, the system can characterise terrain online during normal vehicle operations. Experimental results obtained from different surfaces are presented to validate the system in the field showing its effectiveness and potential benefits to implement adaptive driving assistance systems or to automatically update the parameters of onboard control and planning algorithms.

The paper presents RoboNav, a cost-effective and accurate decimeter-grade navigation system that can be used for deployment in the field of autonomous agricultural robots. The novelty of the system is the reliance on a dual GPS configuration based on two u-blox modules that work in conjunction with three low-cost inertial sensors within a Gaussian Sum Filter able to combine multiple Extended Kalman filters dealing with IMU bias and GPS signal loss. The system provides estimation of both position and heading with high precision and robustness, at a significantly lower cost than existing equivalent navigation systems. RoboNav is validated in a commercial vineyard by performing experimental tests using an all-terrain tracked robot commanded to follow a series of GPS waypoints, trying to minimize the crosstrack error and showing average errors on the order of 0.2 m and 0.2${}^{\circ }$ for the measurement of position and yaw angle, respectively.

The paper deals with the design and testing of a robot for industrial applications featuring omnidirectionality thanks to the use of mecanum wheels. While this architecture provides remarkable manoeuvrability in narrow or cluttered spaces, it has some drawbacks that limit its widespread deployment in practice, especially for heavy-duty and long-duration tasks. As an example, the variability in the mecanum wheel rolling radius leads to undesired dynamic ill-effects, such as slippage and vibrations that affect the accuracy of pose estimation and tracking control systems. Drawing on the modeling of the kinematic and dynamic behaviour of the robot, these effects have been tackled within an adaptive estimation framework that adjusts the robot control system based on the properties of the surface being traversed. The proposed approach has been validated in experimental tests using a physical prototype operating in real industrial settings.