Aaron T. Becker

Approximate Steering of a Unicycle Under Bounded Model Perturbation Using Ensemble Control

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.

Estimating System State During Human Walking With a Powered Ankle-Foot Orthosis

This paper presents a state estimator that reliably detects gait events during human walking with a portable powered ankle-foot orthosis (AFO), based only on measurements of the ankle angle and of contact forces at the toe and heel. Effective control of the AFO critically depends on detecting these gait events. A common approach detects gait events simply by checking if each measurement exceeds a given threshold. Our approach uses cross correlation between a window of past measurements and a learned model to estimate the configuration of the human walker, and detects gait events based on this estimate. We tested our approach in experiments with five healthy subjects and with one subject that had neuromuscular impairment. Using motion capture data for reference, we compared our approach to one based on thresholding and to another common one based on k-nearest neighbors. The results showed that our approach reduced the RMS error by up to 40% for the impaired subject and up to 49% for the healthy subjects. Moreover, our approach was robust to perturbations due to changes in walking speed and to control actuation.

Feedback control of many differential-drive robots with uniform control inputs

In this paper, we derive a globally asymptotically stabilizing feedback control policy for a collection of differential-drive robots under the constraint that every robot receives exactly the same control inputs. We begin by assuming that each robot has a slightly different wheel size, which scales its forward speed and turning rate by a constant that can be found by offline or online calibration. The resulting feedback policy is easy to implement, is robust to standard models of noise, and scales to an arbitrary number (even a continuous ensemble) of robots. We validate this policy with hardware experiments, which additionally reveal that our feedback policy still works when the wheel sizes are unknown and even when the wheel sizes are all approximately identical. These results have possible future application to control of micro- and nano-scale robotic systems, which are often subject to similar constraints.

Controlling many differential-drive robots with uniform control inputs

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.

Automated manipulation of spherical objects in three dimensions using a gimbaled air jet

This paper presents a mechanism and a control strategy that enables automated non-contact manipulation of spherical objects in three dimensions using air flow, and demonstrates several tasks that can be performed with such a system. The mechanism is a 2-DOF gimbaled air jet with a variable flow rate. The control strategy is feedback linearization based on a classical fluid dynamics model with state estimates from stereo vision data. The tasks include palletizing, sorting, and ballistics. All results are verified with hardware experiments.

Simultaneous steering and imaging of magnetic particles using MRI toward delivery of therapeutics

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 27u2009Hz 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 (74u2009mm/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.

Crowdsourcing swarm manipulation experiments: A massive online user study with large swarms of simple robots

Micro- and nanorobotics have the potential to revolutionize many applications including targeted material delivery, assembly, and surgery. The same properties that promise breakthrough solutions - small size and large populations - present unique challenges to generating controlled motion. We want to use large swarms of robots to perform manipulation tasks; unfortunately, human-swarm interaction studies as conducted today are limited in sample size, are difficult to reproduce, and are prone to hardware failures. We present an alternative. This paper examines the perils, pitfalls, and possibilities we discovered by launching SwarmControl.net, an online game where players steer swarms of up to 500 robots to complete manipulation challenges. We record statistics from thousands of players, and use the game to explore aspects of large-population robot control. We present the game framework as a new, open-source tool for large-scale user experiments. Our results have potential applications in human control of micro- and nanorobots, supply insight for automatic controllers, and provide a template for large online robotic research experiments.

Simultaneously Powering and Controlling Many Actuators with a Clinical MRI Scanner

Actuators that are powered, imaged, and controlled by Magnetic Resonance (MR) scanners offer the potential of inexpensively providing wireless control of MR-guided robots. Similar to traditional electric motors, the MR scanner acts as the stator and generates propulsive torques on an actuator rotor containing one or more ferrous particles. The MR scanner can control three orthogonal gradient fields. Prior work demonstrated control of a single actuator rotor. This paper proposes and demonstrates independent, simultaneous control of n rotors. The controller relies on inhomogeneity between rotors, such as ensuring no rotor axes are parallel. This paper provides easily-implemented velocity and position controllers with global asymptotic convergence, and optimization techniques for implementation. Code for simulations and control laws is available online.

Toward tissue penetration by MRI-powered millirobots using a self-assembled Gauss gun

MRI-based navigation and propulsion of millirobots is a new and promising approach for minimally invasive therapies. The strong central field inside the scanner, however, precludes torque-based control. Consequently, prior propulsion techniques have been limited to gradient-based pulling through fluid-filled body lumens. This paper introduces a technique for generating large impulsive forces that can be used to penetrate tissue. The approach is based on navigating multiple robots to a desired location and using self-assembly to trigger the conversion of magnetic potential energy into sufficient kinetic energy to achieve penetration. The approach is illustrated through analytical modeling and experiments in a clinical MRI scanner.

Stochastic swarm control with global inputs

Micro- and nanorobots can be built in large numbers, but generating independent control inputs for each robot is prohibitively difficult. Instead, micro- and nanorobots are often controlled by a global field. In previous work we conducted large-scale human-user experiments where humans played games that steered large swarms of simple robots to complete tasks such as manipulating blocks. One surprising result was that humans completed a block-pushing task faster when provided with only the mean and variance of the robot swarm than with full-state feedback. Inspired by human operators, this paper investigates controllers that use only the mean and variance of a robot swarm. We prove that the mean position is controllable, and show how an obstacle can make the variance controllable. We next derive automatic controllers for these and a hybrid, hysteresis-based switching control to regulate the first two moments of the robot distribution. Finally, we employ these controllers as primitives for a block-pushing task.