In this work, we propose a dynamic landing solution without the need for onboard exteroceptive sensors and an expensive computation unit, where all localization and control modules are carried out on the ground in a non-inertial frame. Our system starts with a relative state estimator of the aerial robot from the perspective of the landing platform, where the state tracking of the UAV is done through a set of onboard LED markers and an on-ground camera; the state is expressed geometrically on manifold, and is returned by Iterated Extended Kalman filter (IEKF) algorithm. Subsequently, a motion planning module is developed to guide the landing process, formulating it as a minimum jerk trajectory by applying the differential flatness property. Considering visibility and dynamic constraints, the problem is solved using quadratic programming, and the final motion primitive is expressed through piecewise polynomials. Through a series of experiments, the applicability of this approach is validated by successfully landing 18 cm x 18 cm quadrotor on a 43 cm x 43 cm platform, exhibiting performance comparable to conventional methods. Finally, we provide comprehensive hardware and software details to the research community for future reference.

In smart electrical grids, fault detection tasks may have a high impact on society due to their economic and critical implications. In the recent years, numerous smart grid applications, such as defect detection and load forecasting, have embraced data-driven methodologies. The purpose of this study is to investigate the challenges associated with the security of machine learning (ML) applications in the smart grid scenario. Indeed, the robustness and security of these data-driven algorithms have not been extensively studied in relation to all power grid applications. We demonstrate first that the deep neural network method used in the smart grid is susceptible to adversarial perturbation. Then, we highlight how studies on fault localization and type classification illustrate the weaknesses of present ML algorithms in smart grids to various adversarial attacks

Iranian-born bioengineer researcher Nima Enayati works on a robotic surgery machine during an interview with the Associated Press at the Polytechnic University of Milan, Italy, Tuesday, Jan. 31, 2017. An Iranian researcher at Milan's Polytechnic University, Enayati was refused check-in Monday at Milan's Malpensa Airport for his U.S.-bound flight on Turkish Airlines after the Trump administration's executive order came down. Iranian-born bioengineer researcher Nima Enayati works on a robotic surgery machine during an interview with the Associated Press at the Polytechnic University of Milan, Italy, Tuesday, Jan. 31, 2017. An Iranian researcher at Milan's Polytechnic University, Enayati was refused check-in Monday at Milan's Malpensa Airport for his U.S.-bound flight on Turkish Airlines after the Trump administration's executive order came down. Iranian-born bioengineer researcher Nima Enayati stands as he works on a robotic surgery machine during an interview with the Associated Press at the Polytechnic University of Milan, Italy, Tuesday, Jan. 31, 2017.

An international team of researchers has trained a robot to imitate natural human actions, in the hope that humans and robots can coordinate their actions during critical events such as surgeries. Researchers from Italy's Polytechnic University of Milan led an international team that trained a robot to imitate natural human actions. The work demonstrates humans and robots can effectively coordinate their actions during high-stakes events such as surgeries. Over time, the research could lead to improvements in safety during medical procedures because robots do not tire and can complete an endless series of precise movements. Robotic co-workers "will just allow us to decrease workload and achieve better performances in several tasks, from medicine to industrial applications," says Polytechnic University of Milan's Elena De Momi.