Reusable Software Components for Multi-Robot Foraging

Swarm intelligence is a rapidly growing area of robotics research that has the potential to reshape traditional approaches in many different fields, including military, agriculture, and medicine. However, a lack of widely available development platforms for swarm applications has hindered progress by forcing researchers to recreate previous efforts. The goal of this MQP is to provide a framework for developers to easily realize their own projects. The focus of this project is on identifying, programming, and evaluating the common behaviors that compose complex tasks such as foraging. The software components we developed can be easily reused and extended by other developers to realize other foraging algorithms

A general architecture for robotic swarms

Swarms are large groups of simplistic individuals that collectively solve disproportionately complex tasks. Individual swarm agents are limited in perception, mechanically simple, have no global knowledge and are cheap, disposable and fallible. They rely exclusively on local observations and local communications. A swarm has no centralised control. These features are typifed by eusocial insects such as ants and termites, who construct nests, forage and build complex societies comprised of primitive agents. This project created the basis of a general swarm architecture for the control of insect-like robots. The Swarm Architecture is inspired by threshold models of insect behaviour and attempts to capture the salient features of the hive in a closely defined computer program that is hardware agnostic, swarm size indifferent and intended to be applicable to a wide range of swarm tasks. This was achieved by exploiting the inherent limitations of swarm agents. Individual insects were modelled as a machine capable only of perception, locomotion and manipulation. This approximation reduced behaviour primitives to a fixed tractable number and abstracted sensor interpretation. Cooperation was achieved through stigmergy and decisions made via a behaviour threshold model. The Architecture represents an advance on previous robotic swarms in its generality - swarm control software has often been tied to one task and robot configuration. The Architecture's exclusive focus on swarms, sets it apart from existing general cooperative systems, which are not usually explicitly swarm orientated. The Architecture was implemented successfully on both simulated and real-world swarms

The Promise of Systems Science in Health Behavior Research: The Example of Studying Drinking Events

This paper is based on my Research Laureate Address to the American Academy of Health Behavior, Portland Oregon, March 4th, 2018. The paper follows the basic content and structure of my address but has been written in a style more consistent with a scientific essay rather than a transcript of a verbatim speech

Genetic components to worker sterility in the honey bee, Apis mellifera

The primary characteristic that defines eusocial species is reproductive division of labour. Honey bee (Apis mellifera) colonies typically have a single reproductive queen and thousands of sterile workers. Here, I review the factors affecting worker reproduction and then contrast the brain gene expression of workers considered either reproductively altruistic (sterile) or selfish (fecund) over a series of time points. I confirmed that although, theoretically, the genes that allow workers to reproduce must be expressed in order for them to do so, it is the environmental cues, such as nutrition and pheromones, that ultimately control worker reproductive status. I then identify a new set of candidate ‘genes for reproductive altruism’ by considering the differential gene expression of reproductive vs. sterile worker brains on each day, and over multiple consecutive time-points. It was determined that a large portion of the identified genes had metabolic function

Bio-inspired optimization in integrated river basin management

Water resources worldwide are facing severe challenges in terms of quality and quantity. It is essential to conserve, manage, and optimize water resources and their quality through integrated water resources management (IWRM). IWRM is an interdisciplinary field that works on multiple levels to maximize the socio-economic and ecological benefits of water resources. Since this is directly influenced by the river’s ecological health, the point of interest should start at the basin-level. The main objective of this study is to evaluate the application of bio-inspired optimization techniques in integrated river basin management (IRBM). This study demonstrates the application of versatile, flexible and yet simple metaheuristic bio-inspired algorithms in IRBM. In a novel approach, bio-inspired optimization algorithms Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to spatially distribute mitigation measures within a basin to reduce long-term annual mean total nitrogen (TN) concentration at the outlet of the basin. The Upper Fuhse river basin developed in the hydrological model, Hydrological Predictions for the Environment (HYPE), is used as a case study. ACO and PSO are coupled with the HYPE model to distribute a set of measures and compute the resulting TN reduction. The algorithms spatially distribute nine crop and subbasin-level mitigation measures under four categories. Both algorithms can successfully yield a discrete combination of measures to reduce long-term annual mean TN concentration. They achieved an 18.65% reduction, and their performance was on par with each other. This study has established the applicability of these bio-inspired optimization algorithms in successfully distributing the TN mitigation measures within the river basin. Stakeholder involvement is a crucial aspect of IRBM. It ensures that researchers and policymakers are aware of the ground reality through large amounts of information collected from the stakeholder. Including stakeholders in policy planning and decision-making legitimizes the decisions and eases their implementation. Therefore, a socio-hydrological framework is developed and tested in the Larqui river basin, Chile, based on a field survey to explore the conditions under which the farmers would implement or extend the width of vegetative filter strips (VFS) to prevent soil erosion. The framework consists of a behavioral, social model (extended Theory of Planned Behavior, TPB) and an agent-based model (developed in NetLogo) coupled with the results from the vegetative filter model (Vegetative Filter Strip Modeling System, VFSMOD-W). The results showed that the ABM corroborates with the survey results and the farmers are willing to extend the width of VFS as long as their utility stays positive. This framework can be used to develop tailor-made policies for river basins based on the conditions of the river basins and the stakeholders' requirements to motivate them to adopt sustainable practices. It is vital to assess whether the proposed management plans achieve the expected results for the river basin and if the stakeholders will accept and implement them. The assessment via simulation tools ensures effective implementation and realization of the target stipulated by the decision-makers. In this regard, this dissertation introduces the application of bio-inspired optimization techniques in the field of IRBM. The successful discrete combinatorial optimization in terms of the spatial distribution of mitigation measures by ACO and PSO and the novel socio-hydrological framework using ABM prove the forte and diverse applicability of bio-inspired optimization algorithms

INVITATION PAPER (C.P. ALEXANDER FUND): FORAGING OF INDIVIDUAL WORKERS IN RELATION TO COLONY STATE IN THE SOCIAL HYMENOPTERA

Workers of social insects are members of colonies that survive and reproduce together. Therefore, the behavioral activities of individual workers should be integrated with colony state. We here summarize and discuss the relationship between colony state and foraging behavior of individual workers under the provisional assumption that the colony is a unit. We argue that colony state can be described by a number of variables that should relate to fitness components in order to be meaningful. Among the possible candidates, colony population size seems to have an overriding importance in many respects, as shown by its relation to fitness components such as survival probability and reproductive performance. Other important variables include colony demography, i.e. caste or size distributions, nutritional status, or queen number. Each of these variables has been shown to affect fitness components; however, the evidence is rather scanty. We also discuss the evidence that variation in colony state variables relates to variation in individual worker behavior. Nutritional status (i.e. low or high levels of food stores) and colony size have been shown repeatedly to affect individual behavior. However, most of the evidence comes from the honey bee. Some studies suggest that behavioral responses are hierarchically structured. More work needs to be done to investigate the actual mechanisms of integration of individual behavior with colony state. Some knowledge has accumulated about the processes that govern recruitment to food sources. We conclude this review by discussing some concepts and problems for further research. These include the concept of a preferred colony state to which the colony should return after disturbance through the behavioral activities of the workers. Further theoretical elaboration and empirical investigations may help to elucidate whether this concept is useful and necessary. A largely neglected issue concerns the number versus effort problem, i.e. whether individuals should work harder or more workers should be allocated to a task that is in demand. We propose a simple scenario that suggests testable predictions. Finally, we discuss how colony state, individual work load, and the dependence of worker mortality rate on activity level may interact to generate different short-term foraging strategies that workers should adop