Luigi Iannelli

Giuseppe Silano, Luigi Iannelli

The main motivation of this work is to propose a simulation approach for a specific task within the Unmanned Aerial Vehicle (UAV) field, i.e., the visual detection and tracking of arbitrary moving objects. In particular, it is described MAT-Fly, a numerical simulation platform for multi-rotor aircraft characterized by the ease of use and control development. The platform is based on Matlab and the MathWorks Virtual Reality (VR) and Computer Vision System (CVS) toolboxes that work together to simulate the behavior of a quad-rotor while tracking a car that moves along a nontrivial path. The VR toolbox has been chosen due to the familiarity that students have with Matlab and because it does not require a notable effort by the user for the learning and development phase thanks to its simple structure. The overall architecture is quite modular so that each block can be easily replaced with others simplifying the code reuse and the platform customization. Some simple testbeds are presented to show the validity of the approach and how the platform works. The simulator is released as open-source, making it possible to go through any part of the system, and available for educational purposes.

Giuseppe Silano, Quentin Sablé, Marco Tognon et al.

This paper presents a sensitivity-based tube Nonlinear Model Predictive Control (NMPC) framework for cooperative aerial chains under bounded parametric uncertainty. We consider a planar two-vehicle chain connected by rigid links, modeled with input-rate actuation to enforce slew-rate and magnitude limits on thrust and torque. Robustness to uncertainty in link mass, length, and inertia is achieved by propagating first-order parametric state sensitivities along the horizon and using them to compute online constraint-tightening margins. We robustify an inter-link separation constraint, implemented via a smooth cosine embedding, and thrust-magnitude bounds. The method is implemented in MATLAB and evaluated with boundary-hugging maneuvers and Monte-Carlo uncertainty sampling. Results show improved constraint margins under uncertainty with tracking performance comparable to nominal NMPC.

Giuseppe Silano, Davide Liuzza, Luigi Iannelli et al.

Inspection of power line infrastructures must be periodically conducted by electric companies in order to ensure reliable electric power distribution. Research efforts are focused on automating the power line inspection process by looking for strategies that satisfy different requirements expressed in terms of potential damage and faults detection. This problem comes up with the need of safe planning and control techniques for autonomous robots to perform visual inspection tasks. Such an application becomes even more interesting and of critical importance when considering a multi-robot extension. In this paper, we propose to compute feasible and constrained trajectories for a fleet of quad-rotors leveraging on Signal Temporal Logic (STL) specifications. The planner allows to formulate rather complex missions avoiding obstacles and forbidden areas along the path. Simulations results achieved in MATLAB show the effectiveness of the proposed approach leading the way to experimental tests on the hardware.