The importance of educating the next generations in the understanding of the fundamentals of the upcoming scientific and technological innovations that will force a broad social and economical paradigm change can not be overstressed. One such breakthrough technologies is Artificial Intelligence (AI), specifically machine learning algorithms. Nowadays, the public has little understanding of the workings and implications of AI techniques that are already entering their lives in many ways. We aim to achieve widespread public understanding of these issues in an experiential learning framework. Following a design based research approach, we propose to implement program coding scaffoldings to teach and experiment some basic mechanisms of AI systems. Such experiments would be shedding new light into AI potentials and limitations. In this paper we focus on innovative ways to introduce high school students to the fundamentals and operation of two of the most popular AI algorithms. We describe the elements of a workshop where we provide an academic use-create-modify scaffolding where students work on the Scratch partial coding of the algorithms so they can explore the behavior of the algorithm, gaining understanding of the underlying computational thinking of AI processes. The extent of the impact on the students of this experience is measured through questionnaires filled before and after participation in the workshop. Preliminary experiments offer encouraging results, showing that the workshop has differential impact on the way students understand AI. INDEX TERMS Scratch programming, teaching AI fundamentals, public AI awareness.

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In this article, a control strategy approach is proposed for a system consisting of a quadrotortransporting a double pendulum. In our case, we attempt to achieve a swing free transportationof the pendulum, while the quadrotor closely follows a specific trajectory. This dynamic system ishighly nonlinear, therefore, the fulfillment of this complex task represents a demanding challenge.Moreover, achieving dampening of the double pendulum oscillations while following a precisetrajectory are conflicting goals. We apply a proportional derivative (PD) and a model predictivecontrol (MPC) controllers for this task. Transportation of a multiple pendulum with an aerial robotis a step forward in the state of art towards the study of the transportation of loads with complexdynamics. We provide the modeling of the quadrotor and the double pendulum. For MPC wedefine the cost function that has to be minimized to achieve optimal control. We report encouragingpositive results on a simulated environmentcomparing the performance of our MPC-PD controlcircuit against a PD-PD configuration, achieving a three fold reduction of the double pendulummaximum swinging angle.

This paper deals with the control of a team of unmanned air vehicles (UAVs), specifically quadrotors, for which their mission is the transportation of a deformable linear object (DLO), i.e., a cable, hose or similar object in quasi-stationary state, while cruising towards destination. Such missions have strong industrial applications in the transportation of hoses or power cables to specific locations, such as the emergency power or water supply in hazard situations such as fires or earthquake damaged structures. This control must be robust to withstand strong and sudden wind disturbances and remain stable after aggressive maneuvers, i.e., sharp changes of direction or acceleration. To cope with these, we have previously developed the online adaptation of the proportional derivative (PD) controllers of the quadrotors thrusters, implemented by a fuzzy logic rule system that experienced adaptation by a stochastic gradient rule. However, sagging conditions appearing when the transporting drones are too close or too far away induce singularities in the DLO catenary models, breaking apart the control system. The paper’s main contribution is the formulation of the hybrid selective model of the DLO sections as either catenaries or parabolas, which allows us to overcome these sagging conditions. We provide the specific decision rule to shift between DLO models. Simulation results demonstrate the performance of the proposed approach under stringent conditions.

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