This paper describes the process of design, analysis, and scoring with both subjective and objective criteria, of a set of possible haptic inceptors configurations to be used as primary flight control devices for a class of future tilt rotor vehicles. Several scoring criteria were adopted like ergonomics, cognitive compatibility, easiness of recovery in fault situations, and expected workload among several others. Each criterion was weighted to obtain a scoring matrix for a total of 18 different configurations, some "classic", some novel, leading to an overall ranking. The matrix was used to select the best candidate configurations to be tested using a human-in-the-loop simulator in the context of the EU Funded CleanSky2 SMARTeR Project. After a final review phase, the best solution will be adopted to actually fly the Leonardo NextGen Civil Tilt Rotor Technological Demonstrator in the context of the EU Funded CleanSky2 SAIS Project.

Haptic guidance has previously been employed to improve human performance in control tasks. This paper presents an experiment to evaluate whether haptic feedback can be used to help humans learn a compensatory tracking task. In the experiment, participants were divided into two groups: the haptic group and the no-aid group. The haptic group performed a first training phase with haptic feedback and a second evaluation phase without haptic feedback. The no-aid group performed the whole experiment without haptic feedback. Results indicated that haptic group achieved better performance than the no-aid group during the training phase. Furthermore, performance of haptic group did not worsen in the evaluation phase when the haptic feedback was turned off. Moreover, the no-aid group needed more experimental trials to achieve similar performance to the haptic group. These findings indicate that haptic feedback helped participants learn the task quicker.

This article proposes a novel solution to the Pose Estimation problem for Ego-Motion from stereo camera images. The approach uses a nonlinear function, derived from the concept of Gibbs' Entropy, which is robust by nature to the presence of noise and outliers in the visual features. The SIFT algorithm is used to collect and match the features from stereo images. The 3-vectors quaternion parameterization is used to parameterize the rotation matrix, in order to avoid the unit norm constraint in the minimization computation. Simulations and experimental results are presented and compared with the results obtained via the classical Iterative Closest Point approach.

The article describes a novel approach for deployment of a swarm of heterogeneous autonomous vehicles. Each vehicle is treated as the agent of a network, which cooperates in order to cover a given area according to its own capabilities. A general framework is introduced, which aims at providing tools for solving a large class of coordination problems. The capabilities of each agent are modeled with Descriptor Functions; the sum of these functions constitutes the swarm's descriptor. The goal of the swarm is to match a desired descriptor, by minimizing an appropriate cost functional. A control law is proposed, which is capable of driving the agents towards the achievement of the goal. The existence of local minima and of a global minimum is discussed. Theoretical results on the existence of the global minimum are given. The Area Coverage problem is selected as a preliminary test for the algorithm. Simulation results show the effectiveness of the proposed approach.