Human Swarm Interaction (HSI) is a new field which relates to the effective control of robotic swarms by human operators. The iterature has shown that the control of swarms can become quite complicated. On the other hand, Brain Machine Interfaces (BMI) can offer intuitive control in a plethora of applications where other interfaces alone (e.g. joysticks) are inadequate or impractical, e.g. for people with motor disabilities. There are multiple types of BMI, but most of them rely on the analysis of ElectroEncephaloGraphic (EEG) signals. The authors have previously shown that swarm behaviors elicit specific brain activity on human subjects that observe them. Motivated by this result, in this work, we present preliminary results of a hybrid BMI that combines information from the brain and an external device. An algorithm for extracting information from the frequency domain of EEG signals that allows integration with the manual task of using a joystick is presented. The hybrid interface shows high accuracy and robustness when used as a brain-robot interface. Moreover, it allows for continuous control variables extracted from the EEG signals. Finally, its efficacy is proven across multiple subjects, while its performance is also demonstrated in the real-time control of a swarm of quadrotors.

This paper presents a vision-based Leader-Follower cooperative scheme which consists of two underwater vehicles in the absence of explicit communications and direct information interchange. A novel method for implicitly calculating the relative pose between the two vehicles is introduced. The absolute and relative localization algorithm is solely based on the observation of visual features projected on the vehicles common workspace and it is performed in a strictly decentralized manner. The cooperation task consists of a Leader vehicle which is tele-operated in an open-loop fashion while inspecting a flat surface and a Follower vehicle which follows the Leader while keeping a fixed 2D distance offset. The Follower is able to track the Leader's motion at all desired configurations via a motion tracking controller designed accordingly. The proposed control scheme for the Follower is relayed on vision-based implicit communications data and has analytically guaranteed stability and convergence properties. The accuracy and reliability of the implicit vision-based localization system, the performance of the designed control scheme, as well as the efficiency of the overall system in the proposed cooperative task, are experimentally verified using two small Remotely Operated Vehicles (ROVs) in a test tank.

In this paper, robustness analysis of constrained Image Based Visual Servoing based on Nonlinear Model Predictive Control (NMPC) is presented. It is known, that real applications such an aerial or a fast underwater robotic systems, suffer from the presence of external disturbances. These kinds of disturbances are inevitable in the physical systems, so it is of great interest to employ robust controllers. Therefore, a rigorous robustness analysis should be conducted. In this paper, the Image Based Visual Servoing system under the MPC framework is proven to be Input-to-State Stable (ISS) and a permissible upper bound of the disturbances is provided. Finally, the validity of the theoretic results is illustrated through a simulated example.