abstract: Robotic swarms can potentially perform complicated tasks such as exploration and mapping at large space and time scales in a parallel and robust fashion. This thesis presents strategies for mapping environmental features of interest – specifically obstacles, collision-free paths, generating a metric map and estimating scalar density fields– in an unknown domain using data obtained by a swarm of resource-constrained robots. First, an approach was developed for mapping a single obstacle using a swarm of point-mass robots with both directed and random motion. The swarm population dynamics are modeled by a set of advection-diffusion-reaction partial differential equations (PDEs) in which a spatially-dependent indicator function marks the presence or absence of the obstacle in the domain. The indicator function is estimated by solving an optimization problem with PDEs as constraints. Second, a methodology for constructing a topological map of an unknown environment was proposed, which indicates collision-free paths for navigation, from data collected by a swarm of finite-sized robots. As an initial step, the number of topological features in the domain was quantified by applying tools from algebraic topology, to a probability function over the explored region that indicates the presence of obstacles. A topological map of the domain is then generated using a graph-based wave propagation algorithm. This approach is further extended, enabling the technique to construct a metric map of an unknown domain with obstacles using uncertain position data collected by a swarm of resource-constrained robots, filtered using intensity measurements of an external signal. Next, a distributed method was developed to construct the occupancy grid map of an unknown environment using a swarm of inexpensive robots or mobile sensors with limited communication. In addition to this, an exploration strategy which combines information theoretic ideas with Levy walks was also proposed. Finally, the problem of reconstructing a two-dimensional scalar field using observations from a subset of a sensor network in which each node communicates its local measurements to its neighboring nodes was addressed. This problem reduces to estimating the initial condition of a large interconnected system with first-order linear dynamics, which can be solved as an optimization problem. / Dissertation/Thesis / Doctoral Dissertation Mechanical Engineering 2018

In this thesis, we study self-organized flocking using a swarm of mobile robots. We first present a mobile robot platform having two novel sensing systems developed specifically for swarm robotic studies. We describe its infrared-based short-range sensing system, capable of measuring the range to obstacles and detecting kin robots. In particular, we describe a novel sensing system called the virtual heading sensor (VHS), which combines a digital compass and a wireless communication module to form a scalable method for sensing the relative headings of neighboring robots. We propose a behavior based on heading alignment and proximal control and show that it is capable of generating self-organized ocking in a group of seven robots. Then, we propose a number of metrics to evaluate the quality of flocking and use them to evaluate four main variants of this behavior. We characterize and model the sensing abilities of the robots and develop a physics-based simulator that is verified against the physical robots for flocking in open environments. After showing in simulation that we can achieve flocking in a group of up to 1000 robots in an open environment, we perform experiments to determine the performance of flocking under different controller parameters and characteristics of VHS using the predefined metrics. In the experiments, we vary the three main characteristics of VHS, namely: (1) The amount and nature of noise in heading measurement, (2) The number of neighboring robots that can be &quot / heard&quot / , and (3) the range of wireless communication. Ourresults show that range of communication is the main factor that determines the scale of flocking, and that the behavior is highly robust against the other two characteristics. We extend an existing particle-based model to determine the phase transition characteristics of flocking under different VHS characteristics. An analytical treatment of the model is also presented and verified against the results obtained from experiments in a physics-based simulator.

Evolutionary methods are shown to be useful in developing behaviors in robotics. Interest in the use of evolution in swarm robotics is also on the rise. However, when one attempts to use artificial evolution to develop behaviors for a swarm robotic system, he is faced with decisions to be made regarding some parameters of fitness evaluations and of the genetic algorithm. In this thesis, aggregation behavior is chosen as a case, where performance and scalability of aggregation behaviors of perceptron controllers that are evolved for a simulated swarm robotic system are systematically studied with different parameter settings. Using a cluster of computers to run simulations in parallel, four experiments are conducted varying some of the parameters. Rules of thumb are derived, which can be of guidance to the use of evolutionary methods to generate other swarm robotic behaviors as well.

abstract: Currently, one of the biggest limiting factors for long-term deployment of autonomous systems is the power constraints of a platform. In particular, for aerial robots such as unmanned aerial vehicles (UAVs), the energy resource is the main driver of mission planning and operation definitions, as everything revolved around flight time. The focus of this work is to develop a new method of energy storage and charging for autonomous UAV systems, for use during long-term deployments in a constrained environment. We developed a charging solution that allows pre-equipped UAV system to land on top of designated charging pads and rapidly replenish their battery reserves, using a contact charging point. This system is designed to work with all types of rechargeable batteries, focusing on Lithium Polymer (LiPo) packs, that incorporate a battery management system for increased reliability. The project also explores optimization methods for fleets of UAV systems, to increase charging efficiency and extend battery lifespans. Each component of this project was first designed and tested in computer simulation. Following positive feedback and results, prototypes for each part of this system were developed and rigorously tested. Results show that the contact charging method is able to charge LiPo batteries at a 1-C rate, which is the industry standard rate, maintaining the same safety and efficiency standards as modern day direct connection chargers. Control software for these base stations was also created, to be integrated with a fleet management system, and optimizes UAV charge levels and distribution to extend LiPo battery lifetimes while still meeting expected mission demand. Each component of this project (hardware/software) was designed for manufacturing and implementation using industry standard tools, making it ideal for large-scale implementations. This system has been successfully tested with a fleet of UAV systems at Arizona State University, and is currently being integrated into an Arizona smart city environment for deployment. / Dissertation/Thesis / Masters Thesis Computer Engineering 2018

In my doctoral dissertation, I tackled two of the main open problems in swarm robotics: design and verification. I did so by using model checking.<p>Designing and developing individual-level behaviors to obtain a desired swarm-level goal is, in general, very difficult, as it is difficult to predict and thus design the non-linear interactions of tens or hundreds individual robots that result in the desired collective behavior. In my dissertation, I presented my novel contribution to the top-down design of robot swarms: property-driven design. Property-driven design is based on prescriptive modeling and model checking. Using property-driven design it is possible to design robot swarms in a systematic way, realizing systems that are "correct by design". I demonstrated property-driven design on two case-studies: aggregation and foraging.<p>Developing techniques to analyze and verify a robot swarm is also a necessary step in order to employ swarm robotics in real-world applications. In my dissertation, I explored the use of model checking to analyze and verify the properties of robot swarms. Model checking allows us to formally describe a set of desired properties of a system, in a more powerful and precise way compared to other mathematical approaches, and verify whether a given model of a system satisfies them. I explored two different approaches: the first based on Bio-PEPA and the second based on KLAIM. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

In this dissertation, we propose and study methods for information transfer within a swarm of mobile robots that coordinately move, or flock, in a common direction. We define information transfer as the process whereby robots share directional information in order to coordinate their heading direction. We identify two paradigms of information transfer: explicit information transfer and implicit information transfer. <p><p>In explicit information transfer, directional information is transferred via communication. Explicit information transfer requires mobile robots equipped with a a communication device. We propose novel communication strategies for explicit information transfer, and we perform flocking experiments in different situations: with one or two desired directions of motion that can be static or change over time. We perform experiments in simulation and with real robots. Furthermore, we show that the same explicit information transfer strategies can also be applied to another collective behavior: collective transport with obstacle avoidance. <p><p>In implicit information transfer, directional information is transferred without communication. We show that a simple motion control method is sufficient to guarantee cohesive and aligned motion without resorting to communication or elaborate<p>sensing. We analyze the motion control method for its capability to achieve flocking with and without a desired direction of motion, both in simulation and using real robots. Furthermore, to better understand its underlying mechanism, we study this<p>method using tools of statistical physics, showing that the process can be explained in terms of non-linear elasticity and energy-cascading dynamics. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

In swarm robotics, the control of a group of robots is often fully distributed and does not rely on any leader. In this thesis, we are interested in understanding how to design collective decision making processes in such groups. Our approach consists in taking inspiration from nature, and especially from self organization in social insects, in order to produce effective collective behaviors in robot swarms. We have devised four robotics experiments that allow us to study multiple facets of collective decision making. The problems on which we focus include cooperative transport of objects, robot localization, resource selection, and resource discrimination. <p><p>We study how information is transferred inside the groups, how collective decisions arise, and through which particular interactions. Important properties of the groups such as scalability, robustness, and adaptivity are also investigated. We show that collective decisions in robot swarms can effectively arise thanks to simple mechanisms of imitation and amplification. We experimentally demonstrate their implementation with direct or indirect information transfer, and with robots that can distinguish the available options partially or not at all. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

We investigate the use of self-assembly in a robotic system as a means of responding<p>to different environmental contingencies. Self-assembly is the mechanism through which<p>agents in a multi-robot system autonomously form connections with one another to create<p>larger composite robotic entities. Initially, we consider a simple response mechanism<p>that uses stochastic self-assembly without any explicit control over the resulting morphology<p> / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

In this thesis, we study how and to what extent a self-organized mobile robot flock can be guided by informing some of the robots within the flock about a preferred direction of motion. Specifically, we extend a flocking behavior that was shown to maneuver a swarm of mobile robots as a cohesive group in free space, avoiding obstacles. In its original form, this behavior does not have a preferred direction and the flock would wander aimlessly. In this study, we incorporate a preference for a goal direction in some of the robots. These informed robots do not signal that they are informed (a.k.a. unacknowledged leadership) and instead guide the swarm by their tendency to move in the desired direction. Through experimental results with physical and simulated robots we show that the self-organized flocking of a robot swarm can be effectively guided by an informed minority of the flock. We evaluate the system using a number of quantitative metrics: First, we propose to use the mutual information metric from Information Theory as a dynamical measure of the information exchange. Then, we discuss the accuracy metric from directional statistics and size of the largest cluster as the measures of system performance. Using these metrics, we perform analyses from two points of views: In the transient analyses, we demonstrate the information exchange between the robots as the time advances, and the increase in the accuracy of the flock when the conditions are suitable for an adequate amount of information exchange. In the steady state analyses, we investigate the interdependent effects of the size of the flock in terms of the robots in it, the ratio of informed robots in the flock over the total flock size, the weight of the direction preference behavior, and the noise in the system.

Swarm robotics for music is a relatively new way to explore algorithmic composition as well as new modes of human robot interaction. This work outlines a strategy for making music with a robotic swarm constrained by acoustic sound, rhythmic music using sequencers, motion causing changes in the music, and finally human and swarm interaction. Two novel simulation programs are created in this thesis: the first is a multi-agent simulation designed to explore suitable parameters for motion to music mappings as well as parameters for real time interaction. The second is a boid-based robotic swarm simulation that adheres to the constraints established, using derived parameters from the multi-agent simulation: orientation, number of neighbors, and speed. In addition, five interaction modes are created that vary along an axis of direct and indirect forms of human control over the swarm motion. The mappings and interaction modes of the swarm robot simulation are evaluated in a user study involving music technology students. The purpose of the study is to determine the legibility of the motion to musical mappings and evaluate user preferences for the mappings and modes of interaction in problem solving and in open-ended contexts. The findings suggest that typical users of a swarm robot system do not necessarily prefer more inherently legible mappings in open-ended contexts. Users prefer direct and intermediate modes of interaction in problem solving scenarios, but favor intermediate modes of interaction in open-ended ones. The results from this study will be used in the design and development of a new swarm robotic system for music that can be used in both contexts.