An Efficient Multiple-Place Foraging Algorithm for Scalable Robot Swarms

Searching and collecting multiple resources from large unmapped environments is an important challenge. It is particularly difficult given limited time, a large search area and incomplete data about the environment. This search task is an abstraction of many real-world applications such as search and rescue, hazardous material clean-up, and space exploration. The collective foraging behavior of robot swarms is an effective approach for this task. In our work, individual robots have limited sensing and communication range (like ants), but they are organized and work together to complete foraging tasks collectively. An efficient foraging algorithm coordinates robots to search and collect as many resources as possible in the least amount of time. In the foraging algorithms we study, robots act independently with little or no central control. As the swarm size and arena size increase (e.g., thousands of robots searching over the surface of Mars or ocean), the foraging performance per robot decreases. Generally, larger robot swarms produce more inter-robot collisions, and in swarm robot foraging, larger search arenas result in larger travel distances causing the phenomenon of diminishing returns. The foraging performance per robot (measured as a number of collected resources per unit time) is sublinear with the arena size and the swarm size. Our goal is to design a scale-invariant foraging robot swarm. In other words, the foraging performance per robot should be nearly constant as the arena size and the swarm size increase. We address these problems with the Multiple-Place Foraging Algorithm (MPFA), which uses multiple collection zones distributed throughout the search area. Robots start from randomly assigned home collection zones but always return to the closest collection zones with found resources. We simulate the foraging behavior of robot swarms in the robot simulator ARGoS and employ a Genetic Algorithm (GA) to discover different optimized foraging strategies as swarm sizes and the number of resources is scaled up. In our experiments, the MPFA always produces higher foraging rates, fewer collisions, and lower travel and search time than the Central-Place Foraging Algorithm (CPFA). To make the MPFA more adaptable, we introduce dynamic depots that move to the centroid of recently collected resources, minimizing transport times when resources are clustered in heterogeneous distributions. Finally, we extend the MPFA with a bio-inspired hierarchical branching transportation network. We demonstrate a scale-invariant swarm foraging algorithm that ensures that each robot finds and delivers resources to a central collection zone at the same rate, regardless of the size of the swarm or the search area. Dispersed mobile depots aggregate locally foraged resources and transport them to a central place via a hierarchical branching transportation network. This approach is inspired by ubiquitous fractal branching networks such as animal cardiovascular networks that deliver resources to cells and determine the scale and pace of life. The transportation of resources through the cardiovascular system from the heart to dispersed cells is the inverse problem of transportation of dispersed resources to a central collection zone through the hierarchical branching transportation network in robot swarms. We demonstrate that biological scaling laws predict how quickly robots forage in simulations of up to thousands of robots searching over thousands of square meters. We then use biological scaling predictions to determine the capacity of depot robots in order to overcome scaling constraints and produce scale-invariant robot swarms. We verify the predictions using ARGoS simulations. While simulations are useful for initial evaluations of the viability of algorithms, our ultimate goal is predicting how algorithms will perform when physical robots interact in the unpredictable conditions of environments they are placed in. The CPFA and the Distributed Deterministic Spiral Algorithm (DDSA) are compared in physical robots in a large outdoor arena. The physical experiments change our conclusion about which algorithm has the best performance, emphasizing the importance of systematically comparing the performance of swarm robotic algorithms in the real world. We illustrate the feasibility of implementing the MPFA with transportation networks in physical robot swarms. Full implementation of the MPFA in an outdoor environment is the next step to demonstrate truly scalable and robust foraging robot swarms

An Approach Based on Particle Swarm Optimization for Inspection of Spacecraft Hulls by a Swarm of Miniaturized Robots

The remoteness and hazards that are inherent to the operating environments of space infrastructures promote their need for automated robotic inspection. In particular, micrometeoroid and orbital debris impact and structural fatigue are common sources of damage to spacecraft hulls. Vibration sensing has been used to detect structural damage in spacecraft hulls as well as in structural health monitoring practices in industry by deploying static sensors. In this paper, we propose using a swarm of miniaturized vibration-sensing mobile robots realizing a network of mobile sensors. We present a distributed inspection algorithm based on the bio-inspired particle swarm optimization and evolutionary algorithm niching techniques to deliver the task of enumeration and localization of an a priori unknown number of vibration sources on a simplified 2.5D spacecraft surface. Our algorithm is deployed on a swarm of simulated cm-scale wheeled robots. These are guided in their inspection task by sensing vibrations arising from failure points on the surface which are detected by on-board accelerometers. We study three performance metrics: (1) proximity of the localized sources to the ground truth locations, (2) time to localize each source, and (3) time to finish the inspection task given a 75% inspection coverage threshold. We find that our swarm is able to successfully localize the present so

The venom composition of the parasitic wasp Chelonus inanitus resolved by combined expressed sequence tags analysis and proteomic approach

<p>Abstract</p> <p>Background</p> <p>Parasitic wasps constitute one of the largest group of venomous animals. Although some physiological effects of their venoms are well documented, relatively little is known at the molecular level on the protein composition of these secretions. To identify the majority of the venom proteins of the endoparasitoid wasp <it>Chelonus inanitus </it>(Hymenoptera: Braconidae), we have randomly sequenced 2111 expressed sequence tags (ESTs) from a cDNA library of venom gland. In parallel, proteins from pure venom were separated by gel electrophoresis and individually submitted to a nano-LC-MS/MS analysis allowing comparison of peptides and ESTs sequences.</p> <p>Results</p> <p>About 60% of sequenced ESTs encoded proteins whose presence in venom was attested by mass spectrometry. Most of the remaining ESTs corresponded to gene products likely involved in the transcriptional and translational machinery of venom gland cells. In addition, a small number of transcripts were found to encode proteins that share sequence similarity with well-known venom constituents of social hymenopteran species, such as hyaluronidase-like proteins and an Allergen-5 protein.</p> <p>An overall number of 29 venom proteins could be identified through the combination of ESTs sequencing and proteomic analyses. The most highly redundant set of ESTs encoded a protein that shared sequence similarity with a venom protein of unknown function potentially specific of the <it>Chelonus </it>lineage. Venom components specific to <it>C. inanitus </it>included a C-type lectin domain containing protein, a chemosensory protein-like protein, a protein related to yellow-e3 and ten new proteins which shared no significant sequence similarity with known sequences. In addition, several venom proteins potentially able to interact with chitin were also identified including a chitinase, an imaginal disc growth factor-like protein and two putative mucin-like peritrophins.</p> <p>Conclusions</p> <p>The use of the combined approaches has allowed to discriminate between cellular and truly venom proteins. The venom of <it>C. inanitus </it>appears as a mixture of conserved venom components and of potentially lineage-specific proteins. These new molecular data enrich our knowledge on parasitoid venoms and more generally, might contribute to a better understanding of the evolution and functional diversity of venom proteins within Hymenoptera.</p

The origin of human pathogenicity and biological interactions in Chaetothyriales

Fungi in the order Chaetothyriales are renowned for their ability to cause human infections. Nevertheless, they are not regarded as primary pathogens, but rather as opportunists with a natural habitat in the environment. Extremotolerance is a major trend in the order, but quite diferent from black yeasts in Capnodiales which focus on endurance, an important additional parameter is advancing toxin management. In the ancestral ecology of rock colonization, the association with metabolite-producing lichens is signifcant. Ant-association, dealing with pheromones and repellents, is another mainstay in the order. The phylogenetically derived family, Herpotrichiellaceae, shows dual ecology in monoaromatic hydrocarbon assimilation and the ability to cause disease in humans and cold-blooded vertebrates. In this study, data on ecology, phylogeny, and genomics were collected and analyzed in order to support this hypothesis on the evolutionary route of the species of Chaetothyriales. Comparing the ribosomal tree with that of enzymes involved in toluene degradation, a signifcant expansion of cytochromes is observed and the toluene catabolism is found to be complete in some of the Herpotrichiellaceae. This might enhance human systemic infection. However, since most species have to be traumatically inoculated in order to cause disease, their invasive potential is categorized as opportunism. Only in chromoblastomycosis, true pathogenicity might be surmised. The criterion would be the possible escape of agents of vertebrate disease from the host, enabling dispersal of adapted genotypes to subsequent generations.info:eu-repo/semantics/publishedVersio

Biologically inspired methods for organizing distributed services on sensor networks

Tales HeimfarthPaderborn, Univ., Diss., 200

Honey Bee Health

Over the past decade, the worldwide decline in honey bee populations has been an important issue due to its implications for beekeeping and honey production. Honey bee pathologies are continuously studied by researchers, in order to investigate the host–parasite relationship and its effect on honey bee colonies. For these reasons, the interest of the veterinary community towards this issue has increased recently, and honey bee health has also become a subject of public interest. Bacteria, such as Melissococcus plutonius and Paenibacillus larvae, microsporidia, such as Nosema apis and Nosema ceranae, fungi, such as Ascosphaera apis, mites, such as Varroa destructor, predatory wasps, including Vespa velutina, and invasive beetles, such as Aethina tumida, are “old” and “new” subjects of important veterinary interest. Recently, the role of host–pathogen interactions in bee health has been included in a multifactorial approach to the study of these insects’ health, which involves a dynamic balance among a range of threats and resources interacting at multiple levels. The aim of this Special Issue is to explore honey bee health through a series of research articles that are focused on different aspects of honey bee health at different levels, including molecular health, microbial health, population genetic health, and the interaction between invasive species that live in strict contact with honey bee populations

Mobility-aware fog computing in dynamic networks with mobile nodes: A survey

Fog computing is an evolving paradigm that addresses the latency-oriented performance and spatio-temporal issues of the cloud services by providing an extension to the cloud computing and storage services in the vicinity of the service requester. In dynamic networks, where both the mobile fog nodes and the end users exhibit time-varying characteristics, including dynamic network topology changes, there is a need of mobility-aware fog computing, which is very challenging due to various dynamisms, and yet systematically uncovered. This paper presents a comprehensive survey on the fog computing compliant with the OpenFog (IEEE 1934) standardised concept, where the mobility of fog nodes constitutes an integral part. A review of the state-of-the-art research in fog computing implemented with mobile nodes is conducted. The review includes the identification of several models of fog computing concept established on the principles of opportunistic networking, social communities, temporal networks, and vehicular ad-hoc networks. Relevant to these models, the contributing research studies are critically examined to provide an insight into the open issues and future research directions in mobile fog computing research

Guidelines for DNA recombination and repair studies: Cellular assays of DNA repair pathways

Understanding the plasticity of genomes has been greatly aided by assays for recombination, repair and mutagenesis. These assays have been developed in microbial systems that provide the advantages of genetic and molecular reporters that can readily be manipulated. Cellular assays comprise genetic, molecular, and cytological reporters. The assays are powerful tools but each comes with its particular advantages and limitations. Here the most commonly used assays are reviewed, discussed, and presented as the guidelines for future studies.European Research Council ERC2014-ADG669898 TARLOOPMinisterio de Economía y Competitividad BFU2016-75058-PJunta de Andalucía BIO123