This paper proposes an algorithm to optimize the walking of humanoid robots based on the inverse kinematic model combined with a Genetic Algorithm. The objectives are to improve the sagittal displacement of the robot and reduce possible lateral deviations during a predetermined path. The foot of the humanoid performs a tapered motion, an approximate ellipse. Horizontal and vertical speeds and the angulation of the humanoid trunk are the input parameters of the algorithm. The algorithm utilizes the input information to calculate the inverse kinematics, and then it submits the obtained result to an evaluation function. We develop a virtual simulator and a robotic platform with 14 degrees of freedom to validate the proposed algorithm. We then test a prototype using the best result obtained in the simulations.

The growing potential for Unmanned Aerial vehicles (UAVs) is creating new business opportunities. Drones, U-space, UTM, and their application to Air Mobility are fostering new applications that evolve quicker than the regulatory framework. For instance, the number of domains of applications is increasing, ranging from infrastructure inspection to parcel deliveries in urban settings. Thus, the number of drones flying simultaneously in the same geographical area is expected to grow over the next few years. It will soon pose safety issues as it might become more and more challenging to ensure safe control of drones so that they are separated from each other and with manned flight operations. The deconfliction or separation management problem, a pressing issue, has been tackled in several research projects. However, there is still an urgent need for a better approach to automate deconfliction at the strategic and tactical levels. Our SESAR-funded project (AI4HyDrop) aims to explore the use of machine learning to develop an intelligent control system to resolve drone deconfliction. To this purpose, we have conducted an extensive literature review, as outlined in this paper. This research was needed to understand better how AI has been explored in the field of UAV’s deconfliction and thus will pave the way for basic concepts that contribute to the requirement elicitation for an AI model-based UAV’s deconfliction.

This working-in-progress paper aims to present a three-dimensional reconstruction using aerial images in different environments. The experiments were conducted with aircraft in both external and internal settings, starting with image acquisition, followed by the application of specific photogrammetry software—both commercial and open-source—and concluding with a qualitative evaluation of the results.