Deploying Sensor Modules with Remotely Operated Underwater Robots for Marine Data Collection

Ocean Big Data (OBD) has become prominent in environmental monitoring, offshore exploration and military surveillance. Secure transfer of this data to the surface can be challenging. Magnetic induction (MI) and visible light communications (VLC) have relatively small ranges. Our approach uses a remotely operated vehicle (ROV) swarm to deploy, recharge and retrieve sensors.Electrical and Computer Engineering, Department ofHonors Colleg

Innovative Robotics for Liquid Environs: MRI Gauss Guns and Drift Nodes

This thesis covers the following two projects: MRI Compatible Gauss Guns (sections I-VI) and Biodegradable Drift Nodes (VII-XI). Project 1: Millirobots propelled and imaged by MRI are a promising approach for minimally invasive therapies. The strong constant magnetic field inside the MRI precludes torque-based control. Consequently, prior propulsion techniques have been limited to gradient-based pulling through fluid-filled body lumens using the weaker magnetic gradient coils. One mechanism to generate additional force to pierce tissue is an MRI Gauss gun, a device that stores magnetic potential energy in an internal arrangement of components. This potential energy can be released through a self-assembly operation. This report presents a new design for an underwater MRI Gauss gun with numerical analysis and results for optimizing the kinetic energy generated. Experiments performed both inside and outside the MRI, in air and underwater validate the optimization analysis. Project 2: As Earth faces environmental changes such as rising sea levels, melting ice caps and increasingly severe tropical storm systems, monitoring of our oceans has become increasingly important. Many of the oceanic environments that require monitoring are vast and dangerous, making the successful deployment and subsequent retrieval of these devices challenging. Ideally, monitoring devices for harsh climates would not require retrieval if they are inexpensive, environmentally benign, and fully degradable. This report describes the design, fabrication and testing of a network of cheap monitoring devices known as Drift Nodes and the ongoing process to make them fully biodegradable, from the 3d-printed housing to the electronics, sensors and batteries.Mechanical Engineering, Department o

Particle computation: complexity, algorithms, and logic

Abstract 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 forward progress is blocked by 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. We provide a wide range of results for a wide range of questions. These can be subdivided into external algorithmic problems, in which particle configurations serve as input for computations that are performed elsewhere, and internal logic problems, in which the particle configurations themselves are used for carrying out computations. For external algorithms, we give both negative and positive results. If we are given a set of stationary obstacles, we prove that it is NP-hard to decide whether a given initial configuration of unit-sized particles can be transformed into a desired target configuration. Moreover, we show that finding a control sequence of minimum length is PSPACE-complete. We also work on the inverse problem, providing constructive algorithms to design workspaces that efficiently implement arbitrary permutations between different configurations. For internal logic, we investigate how arbitrary computations can be implemented. We demonstrate how to encode 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. However, we establish that simulating the full range of complex interactions present in arbitrary digital circuits encounters a fundamental difficulty: a fan-out gate cannot be generated. We resolve this missing component with the help of 2 × 1 particles, which can create fan-out gates that produce multiple copies of the inputs. Using these gates we provide rules for replicating arbitrary digital circuits

Parallel Self-Assembly of Polyominoes Under Uniform Control Inputs

We present fundamental progress on parallel self-assembly using large swarms of microscale particles in complex environments, controlled not by individual navigation, but by a uniform, global, external force with the same effect on each particle. Consider a 2-D grid world, in which all obstacles and particles are unit squares, and for each actuation, particles move maximally until they collide with an obstacle or another particle. We present algorithms that, given an arbitrary 2-D structure, design an obstacle layout. When actuated, this layout generates copies of the input 2-D structure. We analyze the movement and spatial complexity of the factory layouts. We present hardware results on both a macroscale, gravity-based system, and a microscale, magnetically actuated system

Particle computation: complexity, algorithms, and logic

Abstract 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 forward progress is blocked by 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. We provide a wide range of results for a wide range of questions. These can be subdivided into external algorithmic problems, in which particle configurations serve as input for computations that are performed elsewhere, and internal logic problems, in which the particle configurations themselves are used for carrying out computations. For external algorithms, we give both negative and positive results. If we are given a set of stationary obstacles, we prove that it is NP-hard to decide whether a given initial configuration of unit-sized particles can be transformed into a desired target configuration. Moreover, we show that finding a control sequence of minimum length is PSPACE-complete. We also work on the inverse problem, providing constructive algorithms to design workspaces that efficiently implement arbitrary permutations between different configurations. For internal logic, we investigate how arbitrary computations can be implemented. We demonstrate how to encode 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. However, we establish that simulating the full range of complex interactions present in arbitrary digital circuits encounters a fundamental difficulty: a fan-out gate cannot be generated. We resolve this missing component with the help of 2 × 1 particles, which can create fan-out gates that produce multiple copies of the inputs. Using these gates we provide rules for replicating arbitrary digital circuits