This work proposes a feedback control strategy to let the dynamics of a class of under-actuated Vertical TakeOff and Landing (VTOL) aerial vehicle tracking a desired position and attitude trajectory globally with respect to the initial conditions. The proposed feedback controller is derived following an inner-outer loop control paradigm, namely by considering the attitude, which is governed by means of a hybrid controller so as to overcome the well-known topological constraints, serving as a virtual input to stabilize the aircraft position. Two different approaches, the first one obtained by assuming perfect knowledge of the vehicle dynamics and the second one obtained by considering uncertainties and exogenous disturbances, are proposed and compared by analyzing the interconnection between the hybrid attitude and the continuoustime position closed-loop subsystems. The effectiveness of the obtained results is demonstrated by means of simulations and experiments using a miniature quadrotor prototype.

An architecture suitable for the control of multiple unmanned aerial vehicles deployed in Search & Rescue missions is presented in this paper. In the proposed system, a single colocated human operator is able to coordinate the actions of a set of robots in order to retrieve relevant information of the environment. This work is framed in the context of the SHERPA project whose goal is to develop a mixed ground and aerial robotic platform to support search and rescue activities in alpine scenario. Differently from typical human-drone interaction settings, here the operator is not fully dedicated to the drones, but involved in search and rescue tasks, hence only able to provide sparse and incomplete instructions to the robots. In this work, the domain, the interaction framework and the executive system for the autonomous action execution are discussed. The overall system has been tested in a real world mission with two drones equipped with on-board cameras

We propose a shortest trajectory planning algorithm implementation for Unmanned Aerial Vehicles (UAVs) on an embedded GPU. Our goal is the development of a fast, energyefficient global planner for multi-rotor UAVs supporting human operator during rescue missions. The work is based on OpenCL parallel non-deterministic version of the Dijkstra algorithm to solve the Single Source Shortest Path (SSSP). Our planner is suitable for real-time path re-computation in dynamically varying environments of up to 200 m 2. Results demonstrate the efficacy of the approach, showing speedups of up to 74x, saving up to ∼ 98% of energy versus the sequential benchmark, while reaching near-optimal path selection, keeping the average path cost error smaller than 1.2%.

This work presents the design of an open-source control architecture specifically tailored to the rapid development and testing of control and coordination algorithms on micro quadrotors. The proposed design extends an existing open-source and open-hardware quadrotor project, i.e., the Crazyflie nano quadrotor, by adding the guidance, navigation and control layers required to accomplish autonomous flight. The control layer, in particular, is based on a cascade control algorithm able to globally stabilize the position and the attitude of the vehicle. The global property guaranteed by the algorithm allows the quadrotors to perform aggressive maneuvers. The guidance layer is designed to coordinate simultaneously multiple vehicles by generating suitable reference trajectories. Experiments demonstrate the effectiveness of the proposed design. This research has been conducted in part under the collaborative project SHERPA supported by the European Community under the 7th Framework Programme.

Abstract-This paper addresses the problem of generating a path for a fleet of robots navigating in a cluttered environment, while maintaining the so called generalized connectivity. The main challenge in the management of a group of robots is to ensure the coordination between them, taking into account limitations in communication range and sensors, possible obstacles, inter-robot avoidance and other constraints. The Generalized Connectivity Maintenance (GCM) theory already provides a way to represent and consider the aforementioned constraints, but previous works only find solutions via locally-steering functions that do not provide global and optimal solutions. In this work, we merge the GCM theory with randomized pathplanning approaches, and local path optimization techniques to derive a tool that can provide global, good-quality paths. The proposed approach has been intensively tested and verified by mean of numerical simulations.