Swarm robotics consists in using a large number of coordinated autonomous robots, or agents, to accomplish one or more tasks, using local and/or global rules. Individual and collective objectives can be designed for each robot of the swarm. Generally, the agents' interactions exhibit a high degree of complexity that makes it impossible to skip nonlinearities in the model. In this paper, is implemented both a collective interaction using a modified Vicsek model where each agent follows a local group velocity and the individual interaction concerning internal and external obstacle avoidance. The proposed strategies are tested for the migration of a unicycle robot swarm in an unknown environment, where the effectiveness and the migration time are analyzed. To this aim, a new optimal control method for nonlinear dynamical systems and cost functions, named Feedback Local Optimality Principle -FLOP, is applied.
Vertical landing is becoming popular in the last fifteen years, a technology known under the acronym VTVL, Vertical Takeoff and Vertical Landing [1,2]. The interest in such landing technology is dictated by possible cost reductions [3,4], that impose spaceship's recycling. The rockets are not generally designed to perform landing operations, rather their design is aimed at takeoff operations, guaranteeing a very high forward acceleration to gain the velocity needed to escape the gravitational force. In this paper a new control method based on Feedback Local Optimality Principle, named FLOP is applied to the rocket landing problem. The FLOP belongs to a special class of optimal controllers, developed by the mechatronic and vehicle dynamics lab of Sapienza, named Variational Feedback Controllers -VFC, that are part of an ongoing research and are recently applied in different field: nonlinear system, marine and terrestrial autonomous vehicles [5][6][7], multi agents interactions and vibration control [8,9]. The paper is devoted to show the robustness of the nonlinear controlled system, comparing the performances with the LQR, one of the most acknowledged methods in optimal control.
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