Evolution of collective behaviors for a real swarm of aquatic surface robots

Swarm robotics is a promising approach for the coordination of large numbers of robots. While previous studies have shown that evolutionary robotics techniques can be applied to obtain robust and efficient self-organized behaviors for robot swarms, most studies have been conducted in simulation, and the few that have been conducted on real robots have been confined to laboratory environments. In this paper, we demonstrate for the first time a swarm robotics system with evolved control successfully operating in a real and uncontrolled environment. We evolve neural network-based controllers in simulation for canonical swarm robotics tasks, namely homing, dispersion, clustering, and monitoring. We then assess the performance of the controllers on a real swarm of up to ten aquatic surface robots. Our results show that the evolved controllers transfer successfully to real robots and achieve a performance similar to the performance obtained in simulation. We validate that the evolved controllers display key properties of swarm intelligence-based control, namely scalability, flexibility, and robustness on the real swarm. We conclude with a proof-of-concept experiment in which the swarm performs a complete environmental monitoring task by combining multiple evolved controllers.info:eu-repo/semantics/publishedVersio

Terrestrial Mammal Conservation: Global Evidence for the Effects of Interventions for terrestrial mammals excluding bats and primates

Terrestrial Mammal Conservation provides a thorough summary of the available scientific evidence of what is known, or not known, about the effectiveness of all of the conservation actions for wild terrestrial mammals across the world (excluding bats and primates, which are covered in separate synopses). Actions are organized into categories based on the International Union for Conservation of Nature classifications of direct threats and conservation actions. Over the course of fifteen chapters, the authors consider interventions as wide ranging as creating uncultivated margins around fields, prescribed burning, setting hunting quotas and removing non-native mammals.This book is written in an accessible style and is designed to be an invaluable resource for anyone concerned with the practical conservation of terrestrial mammals.The authors consulted an international group of terrestrial mammal experts and conservationists to produce this synopsis. Funding was provided by the MAVA Foundation, Arcadia and National Geographic Big Cats Initiative.Terrestrial Mammal Conservation is the seventeenth publication in the Conservation Evidence Series, linked to the online resource www.ConservationEvidence.com. Conservation Evidence Synopses are designed to promote a more evidence-based approach to biodiversity conservation. Others in the series include Bat Conservation, Primate Conservation, Bird Conservation and Forest Conservation and more are in preparation. Expert assessment of the evidence summarised within synopses is provided online and within the annual publication What Works in Conservation

Northern Bostwana human wildlife coexistence project : project evaluation report

The Northern Botswana Human Wildlife Coexistence Project is a six year project (2010 – 2016) implemented by the Department of Wildlife and National Parks and supported by the Global Environment Facility in partnership with the Government of Botswana. The project has successfully achieved the outcomes for which it was established, namely to develop and test an approach towards mitigating the effects of Human Wildlife Conflict

ACRP Design Competition -- Eagle Eye

This report outlines the concept generation, design, testing, and implementation process of a drone-based automated inspection system. This project was completed for submission in the ACRP Design Competition and for the University of Rhode Island Mechanical Engineering Capstone Design Course. Throughout the course of the year the team was sponsored by their Professor and faculty advisor, Dr. Nassersharif, and worked closely with their airport sponsor, the Rhode Island Airport Corporation. Capstone Design Team 11 was chosen to participate in the Airport Cooperative Research Program (ACRP) National Design Competition. The aim is to plan, design and create innovative approaches to resolve problems experienced by airports and the Federal Aviation Administration (FAA). The team was able to choose between four main categories in which to compete. The category chosen for the competition is the “Airport Management and Planning” category and the “planning for the integration and mitigation of possible impacts of drones into the airport environment” subcategory. The team addressed this subcategory with a solution that automates the daily inspections for runway and taxiway lighting as well as airport perimeter and security of a General Aviation (GA) airport using a drone. The final design was created and validated using Westerly State Airport to complete calculations and perform flight tests. The design is scalable and transferable with the ability to adapt to other GA and private airports, and potentially larger airports. The team demonstrated the adaptability and versatility of the design by also testing the system at Newport State Airport. The design requirements include automating aspects of the daily airfield inspection process and significantly reducing the required man hours to complete the respective inspection tasks. Typical perimeter and security inspections and lighting inspections take approximately one hour to complete. The automated inspection process demonstrated in this project completes each inspection in under 20 minutes. The system uses a video recording feature attached to the drone so that inspections can be logged and archived as well as used as evidence in the event of an incident such as a crash. The design allows for ease of use with a low learning curve to implement and operate the system for different airports. The costs for implementing the system are $4,017. After implementation, airports will save$23,233.5 the first year of operation and $27,250.5 each year thereafter

The Response of Beef Cattle to Disturbances from Unmanned Aerial Vehicles (UAVs)

Unmanned aerial vehicles (UAVs) are increasingly becoming common in animal agriculture. However, research regarding the impact of UAV disturbance on animal wellbeing is lacking or limited. The goal of this study was to investigate the effect of UAV flights on beef cattle by measuring cattle heart and movement rate when introduced to single or multiple UAV flights. A total of 16 -18 crossbred beef heifers were introduced to different flights patterns at between 5 and 9 m above ground level (AGL) at approximately 1 to 2 m/s horizontal velocity for 4 weeks with flights repeated 3 days per week. Results from the study showed that single UAV flights conducted in (i) circular and (ii) grid pattern flights had no significant effect on heifer heart and movement rate. However, multiple (i) circular pattern and (ii) approach style flights increased heifer heart rate when first introduced to UAVs, but repeated flights resulted in habituation. Moreover, heifers first introduced to circular pattern flights were likely to flee but became habituated after repeated flights. However, heifers introduced to approach style flights showed more fleeing behavior even after repeated flights. The findings of this study will provide information for safely using UAVs in cattle health and behavior monitoring

Knowledge-based vision and simple visual machines

The vast majority of work in machine vision emphasizes the representation of perceived objects and events: it is these internal representations that incorporate the 'knowledge' in knowledge-based vision or form the 'models' in model-based vision. In this paper, we discuss simple machine vision systems developed by artificial evolution rather than traditional engineering design techniques, and note that the task of identifying internal representations within such systems is made difficult by the lack of an operational definition of representation at the causal mechanistic level. Consequently, we question the nature and indeed the existence of representations posited to be used within natural vision systems (i.e. animals). We conclude that representations argued for on a priori grounds by external observers of a particular vision system may well be illusory, and are at best place-holders for yet-to-be-identified causal mechanistic interactions. That is, applying the knowledge-based vision approach in the understanding of evolved systems (machines or animals) may well lead to theories and models that are internally consistent, computationally plausible, and entirely wrong

Terrestrial Mammal Conservation

"Terrestrial Mammal Conservation provides a thorough summary of the available scientific evidence of what is known, or not known, about the effectiveness of all of the conservation actions for wild terrestrial mammals across the world (excluding bats and primates, which are covered in separate synopses). Actions are organized into categories based on the International Union for Conservation of Nature classifications of direct threats and conservation actions. Over the course of fifteen chapters, the authors consider interventions as wide ranging as creating uncultivated margins around fields, prescribed burning, setting hunting quotas and removing non-native mammals. This book is written in an accessible style and is designed to be an invaluable resource for anyone concerned with the practical conservation of terrestrial mammals. The authors consulted an international group of terrestrial mammal experts and conservationists to produce this synopsis. Funding was provided by the MAVA Foundation, Arcadia and National Geographic Big Cats Initiative. Terrestrial Mammal Conservation is the seventeenth publication in the Conservation Evidence Series, linked to the online resource www.ConservationEvidence.com. Conservation Evidence Synopses are designed to promote a more evidence-based approach to biodiversity conservation. Others in the series include Bat Conservation, Primate Conservation, Bird Conservation and Forest Conservation and more are in preparation. Expert assessment of the evidence summarised within synopses is provided online and within the annual publication What Works in Conservation.

Multi-agent persistent surveillance under temporal logic constraints

This thesis proposes algorithms for the deployment of multiple autonomous agents for persistent surveillance missions requiring repeated, periodic visits to regions of interest. Such problems arise in a variety of domains, such as monitoring ocean conditions like temperature and algae content, performing crowd security during public events, tracking wildlife in remote or dangerous areas, or watching traffic patterns and road conditions. Using robots for surveillance is an attractive solution to scenarios in which fixed sensors are not sufficient to maintain situational awareness. Multi-agent solutions are particularly promising, because they allow for improved spatial and temporal resolution of sensor information. In this work, we consider persistent monitoring by teams of agents that are tasked with satisfying missions specified using temporal logic formulas. Such formulas allow rich, complex tasks to be specified, such as "visit regions A and B infinitely often, and if region C is visited then go to region D, and always avoid obstacles." The agents must determine how to satisfy such missions according to fuel, communication, and other constraints. Such problems are inherently difficult due to the typically infinite horizon, state space explosion from planning for multiple agents, communication constraints, and other issues. Therefore, computing an optimal solution to these problems is often infeasible. Instead, a balance must be struck between computational complexity and optimality. This thesis describes solution methods for two main classes of multi-agent persistent surveillance problems. First, it considers the class of problems in which persistent surveillance goals are captured entirely by TL constraints. Such problems require agents to repeatedly visit a set of surveillance regions in order to satisfy their mission. We present results for agents solving such missions with charging constraints, with noisy observations, and in the presence of adversaries. The second class of problems include an additional optimality criterion, such as minimizing uncertainty about the location of a target or maximizing sensor information among the team of agents. We present solution methods and results for such missions with a variety of optimality criteria based on information metrics. For both classes of problems, the proposed algorithms are implemented and evaluated via simulation, experiments with robots in a motion capture environment, or both

Seamless Interactions Between Humans and Mobility Systems

As mobility systems, including vehicles and roadside infrastructure, enter a period of rapid and profound change, it is important to enhance interactions between people and mobility systems. Seamless human—mobility system interactions can promote widespread deployment of engaging applications, which are crucial for driving safety and efficiency. The ever-increasing penetration rate of ubiquitous computing devices, such as smartphones and wearable devices, can facilitate realization of this goal. Although researchers and developers have attempted to adapt ubiquitous sensors for mobility applications (e.g., navigation apps), these solutions often suffer from limited usability and can be risk-prone. The root causes of these limitations include the low sensing modality and limited computational power available in ubiquitous computing devices. We address these challenges by developing and demonstrating that novel sensing techniques and machine learning can be applied to extract essential, safety-critical information from drivers natural driving behavior, even actions as subtle as steering maneuvers (e.g., left-/righthand turns and lane changes). We first show how ubiquitous sensors can be used to detect steering maneuvers regardless of disturbances to sensing devices. Next, by focusing on turning maneuvers, we characterize drivers driving patterns using a quantifiable metric. Then, we demonstrate how microscopic analyses of crowdsourced ubiquitous sensory data can be used to infer critical macroscopic contextual information, such as risks present at road intersections. Finally, we use ubiquitous sensors to profile a driver’s behavioral patterns on a large scale; such sensors are found to be essential to the analysis and improvement of drivers driving behavior.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163127/1/chendy_1.pd