Abstractions, Analysis Techniques, and Synthesis of Scalable Control Strategies for Robot Swarms

Tasks that require parallelism, redundancy, and adaptation to dynamic, possibly hazardous environments can potentially be performed very efficiently and robustly by a swarm robotic system. Such a system would consist of hundreds or thousands of anonymous, resource-constrained robots that operate autonomously, with little to no direct human supervision. The massive parallelism of a swarm would allow it to perform effectively in the event of robot failures, and the simplicity of individual robots facilitates a low unit cost. Key challenges in the development of swarm robotic systems include the accurate prediction of swarm behavior and the design of robot controllers that can be proven to produce a desired macroscopic outcome. The controllers should be scalable, meaning that they ensure system operation regardless of the swarm size. This thesis presents a comprehensive approach to modeling a swarm robotic system, analyzing its performance, and synthesizing scalable control policies that cause the populations of different swarm elements to evolve in a specified way that obeys time and efficiency constraints. The control policies are decentralized, computed a priori, implementable on robots with limited sensing and communication capabilities, and have theoretical guarantees on performance. To facilitate this framework of abstraction and top-down controller synthesis, the swarm is designed to emulate a system of chemically reacting molecules. The majority of this work considers well-mixed systems when there are interaction-dependent task transitions, with some modeling and analysis extensions to spatially inhomogeneous systems. The methodology is applied to the design of a swarm task allocation approach that does not rely on inter-robot communication, a reconfigurable manufacturing system, and a cooperative transport strategy for groups of robots. The third application incorporates observations from a novel experimental study of the mechanics of cooperative retrieval in Aphaenogaster cockerelli ants. The correctness of the abstractions and the correspondence of the evolution of the controlled system to the target behavior are validated with computer simulations. The investigated applications form the building blocks for a versatile swarm system with integrated capabilities that have performance guarantees

Computational model of collective nest selection by ants with heterogeneous acceptance thresholds

Collective decision-making is a characteristic of societies ranging from ants to humans. The ant Temnothorax albipennis is known to use quorum sensing to collectively decide on a new home; emigration to a new nest site occurs when the number of ants favouring the new site becomes quorate. There are several possible mechanisms by which ant colonies can select the best nest site among alternatives based on a quorum mechanism. In this study, we use computational models to examine the implications of heterogeneous acceptance thresholds across individual ants in collective nest choice behaviour. We take a minimalist approach to develop a differential equation model and a corresponding non-spatial agent-based model. We show, consistent with existing empirical evidence, that heterogeneity in acceptance thresholds is a viable mechanism for efficient nest choice behaviour. In particular, we show that the proposed models show speed–accuracy trade-offs and speed–cohesion trade-offs when we vary the number of scouts or the quorum threshold

A Recruit's Dilemma: Collective Decision-Making and Information Content in the Ant Temnothorax rugatulus

abstract: An insect society needs to share information about important resources in order to collectively exploit them. This task poses a dilemma if the colony must consider multiple resource types, such as food and nest sites. How does it allocate workers appropriately to each resource, and how does it adapt its recruitment communication to the specific needs of each resource type? In this dissertation, I investigate these questions in the ant Temnothorax rugatulus. In Chapter 1, I summarize relevant past work on food and nest recruitment. Then I describe T. rugatulus and its recruitment behavior, tandem running, and I explain its suitability for these questions. In Chapter 2, I investigate whether food and nest recruiters behave differently. I report two novel behaviors used by recruiters during their interaction with nestmates. Food recruiters perform these behaviors more often than nest recruiters, suggesting that they convey information about target type. In Chapter 3, I investigate whether colonies respond to a tradeoff between foraging and emigration by allocating their workforce adaptively. I describe how colonies responded when I posed a tradeoff by manipulating colony need for food and shelter and presenting both resources simultaneously. Recruitment and visitation to each target partially matched the predictions of the tradeoff hypothesis. In Chapter 4, I address the tuned error hypothesis, which states that the error rate in recruitment is adaptively tuned to the patch area of the target. Food tandem leaders lost followers at a higher rate than nest tandem leaders. This supports the tuned error hypothesis, because food targets generally have larger patch areas than nest targets with small entrances. This work shows that animal groups face tradeoffs as individual animals do. It also suggests that colonies spatially allocate their workforce according to resource type. Investigating recruitment for multiple resource types gives a better understanding of exploitation of each resource type, how colonies make collective decisions under conflicting goals, as well as how colonies manage the exploitation of multiple types of resources differently. This has implications for managing the health of economically important social insects such as honeybees or invasive fire ants.Dissertation/ThesisDoctoral Dissertation Biology 201

Sophisticated collective foraging with minimalist agents: a swarm robotics test

How groups of cooperative foragers can achieve efficient and robust collective foraging is of interest both to biologists studying social insects and engineers designing swarm robotics systems. Of particular interest are distance-quality trade-offs and swarm-size-dependent foraging strategies. Here we present a collective foraging system based on virtual pheromones, tested in simulation and in swarms of up to 200 physical robots. Our individual agent controllers are highly simplified, as they are based on binary pheromone sensors. Despite being simple, our individual controllers are able to reproduce classical foraging experiments conducted with more capable real ants that sense pheromone concentration and follow its gradient. One key feature of our controllers is a control parameter which balances the trade-off between distance selectivity and quality selectivity of individual foragers. We construct an optimal foraging theory model that accounts for distance and quality of resources, as well as overcrowding, and predicts a swarmsize-dependent strategy. We test swarms implementing our controllers against our optimality model and find that, for moderate swarm sizes, they can be parameterised to approximate the optimal foraging strategy. This study demonstrates the sufficiency of simple individual agent rules to generate sophisticated collective foraging behaviour

Deconstructing superorganisms and societies to address big questions in biology

Social insect societies are long-standing models for understanding social behaviour and evolution. Unlike other advanced biological societies (such as the multicellular body), the component parts of social insect societies can be easily deconstructed and manipulated. Recent methodological and theoretical innovations have exploited this trait to address an expanded range of biological questions. We illustrate the broadening range of biological insight coming from social insect biology with four examples. These new frontiers promote open-minded, interdisciplinary exploration of one of the richest and most complex of biological phenomena: sociality

A Bioeconomic Model of Little Fire Ant Wasmannia auropunctata in Hawaii

Reports were scanned in black and white at a resolution of 600 dots per inch and were converted to text using Adobe Paper Capture Plug-in.Wasmannia auropunctata, known as the Little Fire Ant (LFA), was first detected on the island of Hawai‘i (the Big Island) in 1999. It was most probably introduced through imports of contaminated potted plants from mainland USA. We estimate that LFA has now spread to over 4,000 locations on the Big Island and under current management efforts will spread rapidly inundating the Big Island in 15-20 years. Increased efforts in prevention, detections, and mitigation treatments will suppress existing infestations, reduce rate of spread and decrease long term management costs, damages, and human stings. Benefits from increased management are estimated to be $5 billion savings including$540 million in reduced damages and 2.1 billion fewer sting incidents over 35 years.This research was supported in part by the Tropical and Subtropical Agriculture Research (TSTAR) Program (Award Number 2010-34135-21228), The National institute of Food and Agriculture (NIFA), US Department of Agriculture (USDA

Scalable Control Strategies and a Customizable Swarm Robotic Platform for Boundary Coverage and Collective Transport Tasks

abstract: Swarms of low-cost, autonomous robots can potentially be used to collectively perform tasks over large domains and long time scales. The design of decentralized, scalable swarm control strategies will enable the development of robotic systems that can execute such tasks with a high degree of parallelism and redundancy, enabling effective operation even in the presence of unknown environmental factors and individual robot failures. Social insect colonies provide a rich source of inspiration for these types of control approaches, since they can perform complex collective tasks under a range of conditions. To validate swarm robotic control strategies, experimental testbeds with large numbers of robots are required; however, existing low-cost robots are specialized and can lack the necessary sensing, navigation, control, and manipulation capabilities. To address these challenges, this thesis presents a formal approach to designing biologically-inspired swarm control strategies for spatially-confined coverage and payload transport tasks, as well as a novel low-cost, customizable robotic platform for testing swarm control approaches. Stochastic control strategies are developed that provably allocate a swarm of robots around the boundaries of multiple regions of interest or payloads to be transported. These strategies account for spatially-dependent effects on the robots' physical distribution and are largely robust to environmental variations. In addition, a control approach based on reinforcement learning is presented for collective payload towing that accommodates robots with heterogeneous maximum speeds. For both types of collective transport tasks, rigorous approaches are developed to identify and translate observed group retrieval behaviors in Novomessor cockerelli ants to swarm robotic control strategies. These strategies can replicate features of ant transport and inherit its properties of robustness to different environments and to varying team compositions. The approaches incorporate dynamical models of the swarm that are amenable to analysis and control techniques, and therefore provide theoretical guarantees on the system's performance. Implementation of these strategies on robotic swarms offers a way for biologists to test hypotheses about the individual-level mechanisms that drive collective behaviors. Finally, this thesis describes Pheeno, a new swarm robotic platform with a three degree-of-freedom manipulator arm, and describes its use in validating a variety of swarm control strategies.Dissertation/ThesisDoctoral Dissertation Mechanical Engineering 201