Keywords: Control Technology

Keywords: Biologically-inspired methods, Vision, Mapping
Abstract: An event-based camera is a biologically-inspired vision sensor that captures changes in a scene by recording pixel-level events and delivering asynchronous event streams. Event-based cameras excel in capturing fast-moving scenes and high-dynamic range environments, scenarios where conventional cameras often struggle. These characteristics make them well-suited for advanced robotics and computer vision tasks. This paper introduces EVO-RatSLAM: a pioneering biologically-inspired SLAM technique specifically designed for computing visual odometry with event-based cameras. This is the first SLAM approach that utilizes event-based visual odometry to create consistent and robust maps of camera motion over large spatial areas and for extended periods of time. This method, inspired by the computational model of rodents' hippocampus, combines continuous attractor network models with event-based visual inputs to construct cognitive maps. Odometric information is obtained by aligning the events using a probabilistic model. We validate the performance of EVO-RatSLAM through multiple real-world datasets captured using the DVXplorer event-based camera in various scenarios.

Keywords: Control Technology

Keywords: Robotics, Nonlinear robust control
Abstract: This paper presents a robust enhancement to task-priority control methodologies for redundant robotic systems, specifically addressing vehicle-manipulator systems (VMS). A novel control law is proposed, which ensures global exponential stability of the tracking errors in non-singular configurations while providing robust performance under bounded disturbances. Additionally, we establish new, stronger stability guarantees for existing methods. The efficacy of the proposed approach is validated through simulations and a comparative study on a 7-DoF Franka Emika robot manipulator, highlighting the performance of the different control approaches in trajectory tracking and robustness. These advancements contribute to the development of robust, autonomous, and energy-efficient robotic systems for complex applications, such as underwater vehicle-manipulator systems (UVMS), enabling safer and more cost-effective operations.

Keywords: Predictive control, Optimization, Control applications
Abstract: In this work, a Model Predictive Controller (MPC) is proposed to control the plasma shape in the Tokamak à Configuration Variable (TCV). The proposed controller relies on models obtained by coupling linearized plasma response models, derived from the texttt{fge} code of the Matlab EQuilibrium toolbox (MEQ) suite, with a state-space description of the core TCV magnetic control system. It optimizes the reference signals fed to this inner control loop in order to achieve the desired plasma shape while also enforcing constraints on the plant outputs. To this end, a suitable Quadratic Programming (QP) problem is formulated and solved in real-time. The effectiveness of the proposed controller is illustrated through a combination of simulations and experimental results. To the best of our knowledge, this is the first time that a plasma shape control solution based on MPC has been experimentally tested on a real tokamak.