Ricerc@Sapienza

Today robotics has shown many successful strategies to solve several navigation problems. However, moving into a dynamic environment is still a challenging task. This paper presents a novel method for motion generation in dynamic environments based on real-time nonlinear model predictive control (NMPC). At the core of our approach is a least conservative linearized constraint formulation built upon the real-time iteration (RTI) scheme with Gauss- Newton Hessian approximation.

In recent years Reinforcement Learning (RL) has achieved remarkable results. Nonetheless RL algorithms prove to be unsuccessful in robotics applications where constraints satisfaction is involved, e.g. for safety. In this work we propose a control algorithm that allows to enforce constraints over a learned control policy. Hence we combine Nonlinear Model Predictive Control (NMPC) with control-state trajectories generated from the learned policy at each time step. We prove the effectiveness of our method on the Pendubot, a challenging underactuated robot.

The advances in computer processor technology have enabled the application of nonlinear model predictive control (NMPC) to agile systems, such as quadrotors. These sys- tems are characterized by their underactuation, nonlinearities, bounded inputs, and time-delays. Classical control solutions fall short in overcoming these difficulties and fully exploiting the capabilities offered by such platforms.