James McLurkin

Composable continuous-space programs for robotic swarms

Programmability is an increasingly important barrier to the deployment of multi-robot systems, as no prior approach allows routine composition and reuse of general aggregate behaviors. The Proto spatial computing language, however, already provides this sort of aggregate behavior programming for non-mobile systems using an abstraction of the network as a continuous-space-filling device. We extend this abstraction to mobile systems and show that Proto can be applied to multi-robot systems with an actuator that turns a vector field into device motion. Proto programs operate on fields of values over an abstract device called the amorphous medium and can be joined together using functional composition. These programs are then automatically transformed for execution by individual devices, producing an approximation of the specified continuous-space behavior. We are thus able to build up a library of simple swarm behaviors, and to compose them together into highly succinct programs that predictably produce the desired complex swarm behaviors, as demonstrated in simulation and on a group of 40 iRobot SwarmBots.

Massive uniform manipulation: Controlling large populations of simple robots with a common input signal

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.

Using Multi-Robot Systems for Engineering Education: Teaching and Outreach With Large Numbers of an Advanced, Low-Cost Robot

This paper describes the experiences of using an advanced, low-cost robot in science, technology, engineering, and mathematics (STEM) education. It presents three innovations: It is a powerful, cheap, robust, and small advanced personal robot; it forms the foundation of a problem-based learning curriculum; and it enables a novel multi-robot curriculum while fostering collaborative team work on assignments. The robot design has many features specific to educators: It is advanced enough for academic research, has a broad feature set to support a wide range of curricula, and is inexpensive enough to be an effective outreach tool. The low cost allows each student to have their own robot for the semester, so they can work on activities outside the classroom. This robot was used in three different classes in which it was the foundation for an innovative problem-based learning curriculum. In particular, the robot has specialized sensors and a communications system that supports novel multi-robot curricula, which encourage student interaction in new ways. The results are promising; the robot was a big success in graduate, undergraduate, and outreach activities. Finally, student assessments indicate a greater interest and understanding of engineering and other STEM majors, and class evaluations were consistently above average.

Distributed centroid estimation and motion controllers for collective transport by multi-robot systems

This paper presents four distributed motion controllers to enable a group of robots to collectively transport an object towards a guide robot. These controllers include: rotation around a pivot robot, rotation in-place around an estimated centroid of the object, translation, and a combined motion of rotation and translation in which each manipulating robot follows a trochoid path. Three of these controllers require an estimate of the centroid of the object, to use as the axis of rotation. Assuming the object is surrounded by manipulator robots, we approximate the centroid of the object by measuring the centroid of the manipulating robots. Our algorithms and controllers are fully distributed and robust to changes in network topology, robot population, and sensor error. We tested all of the algorithms in real-world environments with 9 robots, and show that the error of the centroid estimation is low, and that all four controllers produce reliable motion of the object.

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.

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.

Reconfiguring Massive Particle Swarms with Limited, Global Control

We investigate algorithmic control of a large swarm of mobile particles (such as robots, sensors, or building material) that move in a 2D workspace using a global input signal such as gravity or a magnetic field. Upon activation of the field, each particle moves maximally in the same direction, until it hits a stationary obstacle or another stationary particle. In an open workspace, this system model is of limited use because it has only two controllable degrees of freedom—all particles receive the same inputs and move uniformly. We show that adding a maze of obstacles to the environment can make the system drastically more complex but also more useful. The resulting model matches ThinkFun’s Tilt puzzle.

Particle computation: Designing worlds to control robot swarms with only global signals

Micro- and nanorobots are often controlled by global input signals, such as an electromagnetic or gravitational field. These fields move each robot maximally until it hits a stationary obstacle or another stationary robot. This paper investigates 2D motion-planning complexity for large swarms of simple mobile robots (such as bacteria, sensors, or smart building material). In previous work we proved it is NP-hard to decide whether a given initial configuration can be transformed into a desired target configuration; in this paper we prove a stronger result: the problem of finding an optimal control sequence is PSPACE-complete. On the positive side, we show we can build useful systems by designing obstacles. We present a reconfigurable hardware platform and demonstrate how to form arbitrary permutations and build a compact absolute encoder. We then take the same platform and use dual-rail logic to build a universal logic gate that concurrently evaluates AND, NAND, NOR and OR operations. Using many of these gates and appropriate interconnects we can evaluate any logical expression.

A robot system design for low-cost multi-robot manipulation.

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

Hexagonal Lattice Formation in Multi-Robot Systems

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.