The concept of navigating groups of magnetically propelled particles through the fluid-filled passageways of the body provides the potential to perform highly localized diagnostic and therapeutic procedures. The use of MRI scanners to both navigate and propel millimeter-scale robots has received recent attention. MRI-based motion planning and control laws have been proposed that can achieve independent position control of multiple robots despite the system being underactuated, i.e., the same magnetic gradients are experienced by all the robots. To date, however, these analyses have neglected the magnetic interaction forces and torques between these robots. Thus, the question of whether or not the relatively weak gradients can stabilize the particles as they approach each other has remained open. This paper investigates this question by analyzing interaction forces and torques experienced inside an MRI scanner. It is shown that interaction torques can be neglected in the presence of the strong central field of the scanner. Using the reduced force-based model, a lower bound on separation distance is derived and it is shown that this bound could be achieved if the system was fully actuated. Considering underactuation, simulation demonstrates that Model Predictive Control can stabilize pairs of robots at separation distances 2.5-3 times the lower bound on separation distance.

It is well known that a real-time visual servoing task which employs a Visual Tracking Algorithm (VTA) imposes high computational cost to robotic system, which consequently results in higher energy consumption and lower autonomy. Motivated by this fact, this paper presents a novel Image Based Visual Servoing-Model Predictive Control (IBVS-MPC) scheme which is combined with a mechanism that decides when the VTA needs to be triggered and new control inputs must be calculated. Between two consecutive triggering instants, the control input trajectory is applied to the robot in an openloop fashion, i.e, no visual measurements and calculation of the control inputs are required during that period. This results in the reduction of the computational effort, energy consumption and increases the autonomy of the system. These factors are of utmost importance in the case of small autonomous robotic systems which perform vision based tasks, such as surveillance and inspection of indoors and outdoors environments. The visibility and inputs constraints, optimality rate of the MPC, as well as the external disturbances, are being considered during the control design. The efficiency of the proposed scheme is demonstrated through a set of real-time experiments using an eye-in-hand mobile robotic system.

This paper presents a novel Vision-based Nonlinear Model Predictive Control (NMPC) scheme for an under-actuated underwater robotic vehicle. In this scheme, the control loop does not close periodically, but instead a self-triggering framework decides when to provide the next control update. Between two consecutive triggering instants, the control sequence computed by the NMPC is applied to the system in an open-loop fashion, i.e, no state measurements are required during that period. This results to a significant smaller number of requested measurements from the vision system, as well as less frequent computations of the control law, reducing in that way the processing time and the energy consumption. The image constraints (i.e preserving the target inside the camera's field of view), the external disturbances induced by currents and waves, as well as the vehicle's kinematic constraints due to under-actuation, are being considered during the control design. The closed-loop system has analytically guaranteed stability and convergence properties, while the performance of the proposed control scheme is experimentally verified using a small under-actuated underwater vehicle 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.

Control of multiple magnetic particles inside the human body may have many potential applications, such as drug delivery in places where conventional procedures cannot reach. A natural candidate for powering as well as tracking these magnetic particles is the MRI scanner. Although it offers the means to control the particles, it also poses difficulties: the magnetic field is applied uniformly to the group, thus making the independent control of each particle a challenging issue, while the actuation and the tracking are interleaved which can result to delays in actuation and measurement. The closed-loop control of a group of millimeter-scale particles, immersed in fluid, driven by the MRI scanner is studied in this paper. More specifically, this problem is presented in a unified manner, handling such issues as delays, constraints, as well as disturbances, and results in a robustly stable controller. We also propose a condition that effectively answers when the system should be in tracking mode versus actuation mode. In addition to theoretical results, the capability of the proposed controller is illustrated through simulation results.