The use of drones in agriculture is becoming more and more popular. The paper presents a novel approach to distinguish between different field's plowing techniques by means of an RGB-D sensor. The presented system can be easily integrated in commercially available Unmanned Aerial Vehicles (UAVs). In order to successfully classify the plowing techniques, two different measurement algorithms have been developed. Experimental tests show that the proposed methodology is able to provide a good classification of the field's plowing depths.

This paper introduces an intelligent system able to perform quality control assessment in an industrial production line. Deep learning techniques are employed and proved successful in a real application for the inspection of welding defects on an assembly line of fuel injectors. Starting from state-of-the-art deep architectures and using the transfer learning technique, it is possible to train a network with about 7 million parameters using a reduced number of injector's images, obtaining an accuracy of 97.22%. The system is also configured in order to exploit new data, collected during operation, to extend the existing dataset and to improve further its performance. The developed system shows that deep neural networks can successfully perform quality inspection tasks that are usually demanded to humans.

The present letter investigates a novel control approach for magnetically driven soft-tethered capsules for colonoscopy-a potentially painless approach for colon inspection. The focus of this work is on a class of devices composed of a magnetic capsule endoscope actuated by a single external permanent magnet. Actuation is achieved by manipulating the external magnet with a serial manipulator, which in turn produces forces and torques on the internal magnetic capsule. We propose a control strategy which, counteracting gravity, achieves levitation of the capsule. This technique, based on a nonlinear backstepping approach, is able to limit contact with the colon walls, reducing friction, avoiding contact with internal folds, and facilitating the inspection of nonplanar cavities. The approach is validated on an experimental setup, which embodies a general scenario faced in colonoscopy. The experiments show that we can attain 19.5% of contact with the colon wall, compared to the almost 100% of previously proposed approaches. Moreover, we show that the control can be used to navigate the capsule through a more realistic environment-a colon phantom-with reasonable completion time.

Teleoperation in robotic embodiments allows operators to perform and program manipulation tasks with better accuracy, dexterity, and visualization than what is possible with traditional human–robot interaction paradigms. However, the perception of cues (e.g., egocentric distances) relevant to task execution, is known to be distorted in virtual environments due to many factors, which can be grouped into technical, human, and methodological categories. This phenomenon becomes more pronounced in a low-cost/encumbrance setup, where the dynamic environment is captured with color and depth (RGB-D) cameras and presented in a virtual environment. In this paper, the effects of augmented reality (AR) are evaluated as a tool to deliver additional information, which helps in overcoming the differences in perception between telepresence and actual presence. The AR feedback is used to improve the embodiment illusion and to guide the operator during task execution. The AR setup, comprising an RGB-D camera and a head-mounted display, is integrated with the Baxter robot and evaluated by involving 22 participants in an experiment while they execute a pick-and-place task, taking into account their expertise in AR/virtual reality (VR) and gaming. The use of AR results in enhancing the accuracy and efficiency of the task performance, besides significantly reducing the effect of the differences in skillfulness between the participants. Furthermore, it is found that the sense of presence and embodiment for the participant is positively affected by different types of AR.

The development of natural interfaces for human-robot interaction provides the user an intuitive way to control and guide robots. In this paper, we propose a novel ROS (Robot Operating System)-integrated interface for remote control that allows the user to teleoperate the robot using his hands motion. The user can adjust online the autonomy of the robot between two levels: direct control and waypoint following. The hand tracking and gestures recognition capabilities of the Leap Motion device are exploited to generate the control commands. The user receives a real-time 3D augmented visual feedback using a Kinect sensor and a HMD. To assess the practicability of the system experimental results are presented using as a benchmark the remote control of a Kuka Youbot.