Path planning techniques are of major importance for the motion of autonomous systems. In addition, the chosen path, safety, and computational burden are essential for ensuring the successful application of such strategies in the presence of obstacles. In this context, this work introduces a modified potential field method that is capable of providing obstacle avoidance, as well as eliminating local minima problems and oscillations in the influence threshold of repulsive fields. A three-dimensional (3D) vortex field is introduced for this purpose so that each robot can choose the best direction of the vortex field rotation automatically and independently according to its position with respect to each object in the workspace. A scenario that addresses swarm flight with sequential cooperation and the pursuit of moving targets in dynamic environments is proposed. Experimental results are presented and thoroughly discussed using a Crazyflie 2.0 aircraft associated with the loco positioning system for state estimation. It is effectively demonstrated that the proposed algorithm can generate feasible paths while taking into account the aforementioned problems in real-time applications.

Technological advances in electromechanical sensor and actuators, energy storage, data processing and control methodology made possible the development of unmanned aerial vehicles (UAVs). The quadrotor has emerged as one of these research platforms due to its mechanical simplicity, high maneuverability, as well as its capability of hovering and perform vertical take-off and landing.However, such vehicle presents some challenging issues to control area, like: nonlinearity, time-varying behavior, in addition it belongs to the class of underactuated mechanical systems, and it is subject to aerodynamics disturbances and parametric uncertainties. Therefore, this dissertation has as main objective, contribute to development and control strategies implementation to solve the positioning and path tracking problems of unmanned aerial vehicles, focusing on an underactuated mechanical system.In order to obtain a dynamic model that represents the aerial vehicle properly and realistically, the motion equations were developed based on the physics laws that define the mechanical system. The model we obtained is decoupled, thus we consider the presence of two subsystems, the rotational and the translational ones.A cascade control strategy was implemented, so that, a model predictive control (MPC) was developed to the altitude and orientation control of the aerial vehicle. However, the positioning control along the xy axis was performed through a proportional integral derivative control (PID). Such strategies allowed a smooth path tracking, beyond the possibility of dealing with physical constraints of the system. In order to assess the robustness of the control structure shown, we performed flight simulations under the presence of aerodynamics disturbances and parametric uncertainties.

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In this paper, we present the new frequency spectrum recurrence analysis technique by means of electro-encephalon signals (EES) analyses. The technique is suitable for time series analysis with noise and disturbances. EES were collected, and alpha waves of the occipital region were analysed by comparing the signals from participants in two states, eyes open and eyes closed. Firstly, EES were characterized and analysed by means of techniques already known to compare with the results of the innovative technique that we present here. We verified that, standard recurrence quantification analysis by means of EES time series cannot statistically distinguish the two states. However, the new frequency spectrum recurrence quantification exhibit quantitatively whether the participants have their eyes open or closed. In sequence, new quantifiers are created for analysing the recurrence concentration on frequency bands. These analyses show that EES with similar frequency spectrum have different recurrence levels revealing different behaviours of the nervous system. The technique can be used to deepen the study on depression, stress, concentration level and other neurological issues and also can be used in any complex system.

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