This paper considers the problem of steering a nonholonomic unicycle despite model perturbation that scales both the forward speed and the turning rate by an unknown but bounded constant. We model the unicycle as an ensemble control system, show that this system is ensemble controllable, and derive an approximate steering algorithm that brings the unicycle to within an arbitrarily small neighborhood of any given Cartesian position. We apply our work to a differential-drive robot with unknown but bounded wheel radius and validate our approach with hardware experiments.

This article presents a miniature magnetic swimmer and a control apparatus able to perform both 3-D path following and blood clot removal. The robots are 2.5 mm in diameter, 6 mm in length, contain an internal permanent magnet, and have cutting tips coated in diamond powder. The robots are magnetically propelled by an external magnetic system using three coil pairs arranged orthogonally. A range of robot tip designs were tested for abrading human blood clots in vitro. The best design removed a blood clot at a maximum rate of 20.13 mm $^3$ /min. A controller for 3-D navigation is presented and tested. The best prototype was used in an experiment that combined both 3-D path following and blood clot removal.

Roboticists, biologists, and chemists are now producing large populations of simple robots, but controlling large populations of robots with limited capabilities is difficult, due to communication and onboard-computation constraints. Direct human control of large populations seems even more challenging. In this paper we investigate control of mobile robots that move in a 2D workspace using three different system models. We focus on a model that uses broadcast control inputs specified in the global reference frame. In an obstacle-free workspace this system model is uncontrollable because it has only two controllable degrees of freedom - all robots receive the same inputs and move uniformly. We prove that adding a single obstacle can make the system controllable, for any number of robots. We provide a position control algorithm, and demonstrate through extensive testing with human subjects that many manipulation tasks can be reliably completed, even by novice users, under this system model, with performance benefits compared to the alternate models. We compare the sensing, computation, communication, time, and bandwidth costs for all three system models. Results are validated with extensive simulations and hardware experiments using over 100 robots.

Magnetic resonance navigation (MRN) offers the potential for real-time steering of drug particles and cells to targets throughout the body. In this technique, the magnetic gradients of an MRI scanner perform image-based steering of magnetically-labelled therapeutics through the vasculature and into tumours. A major challenge of current techniques for MRN is that they alternate between pulse sequences for particle imaging and propulsion. Since no propulsion occurs while imaging the particles, this results in a significant reduction in imaging frequency and propulsive force. We report a new approach in which an imaging sequence is designed to simultaneously image and propel particles. This sequence provides a tradeoff between maximum propulsive force and imaging frequency. In our reported example, the sequence can image at 27 Hz while still generating 95% of the force produced by a purely propulsive pulse sequence. We implemented our pulse sequence on a standard clinical scanner using millimetre-scale particles and demonstrated high-speed (74 mm/s) navigation of a multi-branched vascular network phantom. Our study suggests that the magnetic gradient magnitudes previously demonstrated to be sufficient for pure propulsion of micron-scale therapeutics in magnetic resonance targeting (MRT) could also be sufficient for real-time steering of these particles.

Multi-robot manipulation allows for scalable environmental interaction, which is critical for multi-robot systems to have an impact on our world. A successful manipulation model requires cost-effective robots, robust hardware, and proper system feedback and control. This paper details key sensing and manipulator capabilities of the r-one robot. The r-one robot is an advanced, open source, low-cost platform for multi-robot manipulation and sensing that meets all of these requirements. The parts cost is around $250 per robot. The r-one has a rich sensor suite, including a flexible IR communication/localization/obstacle detection system, high-precision quadrature encoders, gyroscope, accelerometer, integrated bump sensor, and light sensors. Two years of working with these robots inspired the development of an external manipulator that gives the robots the ability to interact with their environment. This paper presents an overview of the r-one, the r-one manipulator, and basic manipulation experiments to illustrate the efficacy our design. The advanced design, low cost, and small size can support university research with large populations of robots and multi-robot curriculum in computer science, electrical engineering, and mechanical engineering. We conclude with remarks on the future implementation of the manipulators and expected work to follow.

This paper derives both open-loop and closed-loop control policies that steer a finite set of differential-drive robots to desired positions in a two-dimensional workspace, when all robots receive the same control inputs but each robot turns at a slightly different rate. In the absence of perturbation, the open-loop policy achieves zero error in finite time. In the presence of perturbation, the closed-loop policy is globally asymptotically stabilizing with state feedback. Both policies were validated with hardware experiments using up to 15 robots. These experimental results suggest that similar policies might be applied to control micro- and nanoscale robotic systems, which are often subject to similar constraints.