Animating Multiple Escape Maneuvers for a School of Fish
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Flocks and Crowds
Flocks and Crowds Flocks and crowds are other essential concepts we'll be exploring in this book. amount of realism to your simulation in just a few lines of code. Crowds can be a bit more complex, but we'll be exploring some of the powerful tools that come bundled with Unity to get the job done. In this chapter, we'll cover the following topics: Learning the history of flocks and herds Understanding the concepts behind flocks Flocking using the Unity concepts Flocking using the traditional algorithm Using realistic crowds the technology being the swarm of bats in Batman Returns in 1992, for which he won an and accurate, the algorithm is also very simple to understand and implement. [ 115 ] Understanding the concepts behind As with them to the real-life behaviors they model. As simple as it sounds, these concepts birds exhibit in nature, where a group of birds follow one another toward a common on the group. We've explored how singular agents can move and make decisions large groups of agents moving in unison while modeling unique movement in each Island. This demo came with Unity in Version 2.0, but has been removed since Unity 3.0. For our project. the way, you'll notice some differences and similarities, but there are three basic the algorithm's introduction in the 80s: Separation: This means to maintain a distance with other neighbors in the flock to avoid collision. The following diagram illustrates this concept: Here, the middle boid is shown moving in a direction away from the rest of the boids, without changing its heading [ 116 ] Alignment: This means to move in the same direction as the flock, and with the same velocity. -
Research Underway: Bangarang Data Entry Program ~ Users' Manual
RU: Bangarang 1.0 Users Manual Keen 2013 Research Underway: Bangarang Data Entry Program ~ Users’ Manual Developed by Eric Keen Scripps Institution of Oceanography Summer 2013 [email protected] CONTENTS_________________________________________________________________________________________ Introduction ………………………………………. 1 Where to Download………………………………. 1 Getting Started……………………………………. 1 Output: Overview………………………………… 3 Output: Standard Fields……………………....... 4 Output: Event Details ………………………….. 5 Appendix 1: Example output………………........ 24 Appendix 2: RUB Abbreviations……………….. 25 INTRODUCTION____________________________________________________________________________________ RU Bangarang (RUB) is a data entry program designed specifically for my dissertation work in the northern Great Bear Fjordland. It is designed to be an easily viewed, intuitive, button-based way of entering observations while letting the computer take care of associating each entry with the necessary but intensive logistical data (time, gps coordinates, current conditions, observers on board, effort status, etc.). The forms are designed to be navigated by using the mouse, using the tab key, or using the touch screen. It is also designed to output these data in a single file that can serve as both a raw record of the day's work and a single source from which to draw certain types of data (e.g. locations of hydrophone recordings and the locations of sighted whales). These, at least, were my intentions. The program was written in Visual Basic 2010 Express and published as a stand-alone executable file (.exe) for Windows. The program outputs text files to a folder it creates on the C directory of your computer. In addition to making this program available to others, I have provided the project files for the program, as well as my R scripts for analyzing the output text files, on my website. -
Swarm Intelligence
Swarm Intelligence Leen-Kiat Soh Computer Science & Engineering University of Nebraska Lincoln, NE 68588-0115 [email protected] http://www.cse.unl.edu/agents Introduction • Swarm intelligence was originally used in the context of cellular robotic systems to describe the self-organization of simple mechanical agents through nearest-neighbor interaction • It was later extended to include “any attempt to design algorithms or distributed problem-solving devices inspired by the collective behavior of social insect colonies and other animal societies” • This includes the behaviors of certain ants, honeybees, wasps, cockroaches, beetles, caterpillars, and termites Introduction 2 • Many aspects of the collective activities of social insects, such as ants, are self-organizing • Complex group behavior emerges from the interactions of individuals who exhibit simple behaviors by themselves: finding food and building a nest • Self-organization come about from interactions based entirely on local information • Local decisions, global coherence • Emergent behaviors, self-organization Videos • https://www.youtube.com/watch?v=dDsmbwOrHJs • https://www.youtube.com/watch?v=QbUPfMXXQIY • https://www.youtube.com/watch?v=M028vafB0l8 Why Not Centralized Approach? • Requires that each agent interacts with every other agent • Do not possess (environmental) obstacle avoidance capabilities • Lead to irregular fragmentation and/or collapse • Unbounded (externally predetermined) forces are used for collision avoidance • Do not possess distributed tracking (or migration) -
Robot Swarm Based on Ant Foraging Hypothesis with Adaptive Lèvy Flights
Robot Swarm based on Ant Foraging Hypothesis with Adaptive Lèvy Flights A thesis submitted to the Graduate School of the University of Cincinnati in partial fulfillment of the requirements of the degree of Master of Science in the Department of Mechanical and Materials Engineering of the College of Engineering and Applied Sciences by Aditya Milind Deshpande B.E. University of Pune August 2014 Committee Chair: Dr. Manish Kumar, Ph.D. Abstract Robot Swarm based on Ant Foraging Hypothesis with Adaptive Levy´ Flights by Aditya Milind Deshpande Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Master of Science Degree in Department of Mechanical and Materials Engineering University of Cincinnati June 2017 Design of robot swarms inspired by self-organization in social insect groups is currently an active research area with a diverse portfolio of potential applications. This thesis is focused on the development of control laws for swarm of robots inspired by ant foraging. Particularly, this work presents control laws for efficient area coverage by a robot swarm in a 2D spatial domain, inspired by the unique dynamical characteristics of ant foraging. The novel idea pursued in the effort is that dynamic, adaptive switching between Brownian motion and Levy´ flight in the stochastic component of the search increases the efficiency of the search and area coverage. The study is motivated by behaviors of certain biological studies who exhibit searching patterns modeled using Levy´ flight. Influence of different pheromone (the virtual chemotactic agent that drives the foraging) threshold values for switching between Levy´ flights and Brownian motion is studied using two performance metrics - area coverage and visit entropy. -
The Riddle of Spinosaurus Aegyptiacus' Dorsal Sail
Geol. Mag. 153 (3), 2016, pp. 544–547. c Cambridge University Press 2015. This is an Open Access article, distributed 544 under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited. doi:10.1017/S0016756815000801 RAPID COMMUNICATION The riddle of Spinosaurus aegyptiacus’ dorsal sail ∗ JAN GIMSA †, ROBERT SLEIGH‡ & ULRIKE GIMSA§ ∗ University of Rostock, Chair for Biophysics, Gertrudenstr. 11A, 18057 Rostock, Germany ‡Sleigh Technical Translations, Wundstrasse 5, 14059 Berlin, Germany §Leibniz Institute for Farm Animal Biology, Institute of Behavioural Physiology, Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany (Received 24 February 2015; accepted 28 September 2015; first published online 17 November 2015) Abstract A second, that the spines supported a muscle or fat-lined hump (Bailey, 1997) was dismissed in favour of Stromer’s Spinosaurus aegyptiacus was probably the largest predatory (1915) hypothesis of convergent evolution with the skin- dinosaur of the Cretaceous period. A new study shows that it covered neural spines of the crested chameleon. Based on the was a semiaquatic hunter. The function of Spinosaurus’ huge idea that the sail was tightly enveloped in skin, the authors dorsal ‘sail’ remains unsolved, however. Three hypotheses proposed that it was used largely for display on land and in have been proposed: (1) thermoregulation; (2) humpback water to deter foes and competitors or to impress potential storage; or (3) display. According to our alternative hypo- sexual partners, and that it would have remained visible while thesis, the submerged sail would have improved manoeuv- swimming. -
Fishing Padre in September by Charles A. Golla
of the water. Trout, redfish and whiting fol- low the bait activity from below, under the action. As the feeding activity intensifies, the bait ball will move in and out of casting range. The longer bait ball stays together, the greater the chance that jack crevalle and tarpon will move in around the edges. Bull and blacktip sharks will follow the jack crevalle. If the bait ball is moving north or south and in and out of casting range, the feeding frenzy is relatively fresh. As the bait wears down, it starts moving in a cir- cular pattern and indicates the frenzy has gone on for some time. It is at this point that you have the greatest chance of see- Charles Golla with Spanish Mackerel ing tarpon and a large concentration of Photo by Charles Golla game fish. Mullet are good indicators of structure and the presence of game fish. If you Fishing Padre in September by Charles A. Golla see mullet lazily moving through one of the bars, there’s a good chance that no September is one of the best months for This includes locating bait concentrations game fish are present. If you see mullet fishing the Padre Island National Seashore and reading the surf structure. all pushed up along the edge or frantically (PINS) because the Fall baitfish migration Baitfish, Birds, and Slicks jumping as they pass over a particular bar is in full force and hungry game fish are Fishing at this time of the year will be or structure, game fish are close by feed- sure to follow. -
Counter-Misdirection in Behavior-Based Multi-Robot Teams *
Counter-Misdirection in Behavior-based Multi-robot Teams * Shengkang Chen, Graduate Student Member, IEEE and Ronald C. Arkin, Fellow, IEEE Abstract—When teams of mobile robots are tasked with misdirection approach using a novel type of behavior-based different goals in a competitive environment, misdirection and robot agents: counter-misdirection agents (CMAs). counter-misdirection can provide significant advantages. Researchers have studied different misdirection methods but the II. BACKGROUND number of approaches on counter-misdirection for multi-robot A. Robot Deception systems is still limited. In this work, a novel counter-misdirection approach for behavior-based multi-robot teams is developed by Robotic deception can be interpreted as robots using deploying a new type of agent: counter misdirection agents motions and communication to convey false information or (CMAs). These agents can detect the misdirection process and conceal true information [4],[5]. Deceptive behaviors can have “push back” the misdirected agents collaboratively to stop the a wide range of applications from military [6] to sports [7]. misdirection process. This approach has been implemented not only in simulation for various conditions, but also on a physical Researchers have studied robotic deception previously [4] robotic testbed to study its effectiveness. It shows that this through computational approaches [8] in a wide range of areas approach can stop the misdirection process effectively with a from human–robot interaction (HRI) [9]–[11] to ethical issues sufficient number of CMAs. This novel counter-misdirection [4]. Shim and Arkin [5] proposed a taxonomy of robot approach can potentially be applied to different competitive deception with three basic dimensions: interaction object, scenarios such as military and sports applications. -
Boids Algorithm in Economics and Finance a Lesson from Computational Biology
University of Amsterdam Faculty of Economics and Business Master's thesis Boids Algorithm in Economics and Finance A Lesson from Computational Biology Author: Pavel Dvoˇr´ak Supervisor: Cars Hommes Second reader: Isabelle Salle Academic Year: 2013/2014 Declaration of Authorship The author hereby declares that he compiled this thesis independently, using only the listed resources and literature. The author also declares that he has not used this thesis to acquire another academic degree. The author grants permission to University of Amsterdam to reproduce and to distribute copies of this thesis document in whole or in part. Amsterdam, July 18, 2014 Signature Bibliographic entry Dvorˇak´ , P. (2014): \Boids Algorithm in Economics and Finance: A Les- son from Computational Biology." (Unpublished master's thesis). Uni- versity of Amsterdam. Supervisor: Cars Hommes. Abstract The main objective of this thesis is to introduce an ABM that would contribute to the existing ABM literature on modelling expectations and decision making of economic agents. We propose three different models that are based on the boids model, which was originally designed in biology to model flocking be- haviour of birds. We measure the performance of our models by their ability to replicate selected stylized facts of the financial markets, especially those of the stock returns: no autocorrelation, fat tails and negative skewness, non- Gaussian distribution, volatility clustering, and long-range dependence of the returns. We conclude that our boids-derived models can replicate most of the listed stylized facts but, in some cases, are more complicated than other peer ABMs. Nevertheless, the flexibility and spatial dimension of the boids model can be advantageous in economic modelling in other fields, namely in ecological or urban economics. -
Animating Predator and Prey Fish Interactions
Georgia State University ScholarWorks @ Georgia State University Computer Science Dissertations Department of Computer Science 5-6-2019 Animating Predator and Prey Fish Interactions Sahithi Podila Follow this and additional works at: https://scholarworks.gsu.edu/cs_diss Recommended Citation Podila, Sahithi, "Animating Predator and Prey Fish Interactions." Dissertation, Georgia State University, 2019. https://scholarworks.gsu.edu/cs_diss/149 This Dissertation is brought to you for free and open access by the Department of Computer Science at ScholarWorks @ Georgia State University. It has been accepted for inclusion in Computer Science Dissertations by an authorized administrator of ScholarWorks @ Georgia State University. For more information, please contact [email protected]. ANIMATING PREDATOR AND PREY FISH INTERACTIONS by SAHITHI PODILA Under the Direction of Ying Zhu, PhD ABSTRACT Schooling behavior is one of the most salient social and group activities among fish. They form schools for social reasons like foraging, mating and escaping from predators. Animating a school of fish is difficult because they are large in number, often swim in distinctive patterns that is they take the shape of long thin lines, squares, ovals or amoeboid and exhibit complex coordinated patterns especially when they are attacked by a predator. Previous work in computer graphics has not provided satisfactory models to simulate the many distinctive interactions between a school of prey fish and their predator, how does a predator pick its target? and how does a school of fish react to such attacks? This dissertation presents a method to simulate interactions between prey fish and predator fish in the 3D world based on the biological research findings. -
Flocks, Herds, and Schools: a Distributed Behavioral Model 1
Published in Computer Graphics, 21(4), July 1987, pp. 25-34. (ACM SIGGRAPH '87 Conference Proceedings, Anaheim, California, July 1987.) Flocks, Herds, and Schools: A Distributed Behavioral Model 1 Craig W. Reynolds Symbolics Graphics Division [obsolete addresses removed 2] Abstract The aggregate motion of a flock of birds, a herd of land animals, or a school of fish is a beautiful and familiar part of the natural world. But this type of complex motion is rarely seen in computer animation. This paper explores an approach based on simulation as an alternative to scripting the paths of each bird individually. The simulated flock is an elaboration of a particle system, with the simulated birds being the particles. The aggregate motion of the simulated flock is created by a distributed behavioral model much like that at work in a natural flock; the birds choose their own course. Each simulated bird is implemented as an independent actor that navigates according to its local perception of the dynamic environment, the laws of simulated physics that rule its motion, and a set of behaviors programmed into it by the "animator." The aggregate motion of the simulated flock is the result of the dense interaction of the relatively simple behaviors of the individual simulated birds. Categories and Subject Descriptors: 1.2.10 [Artificial Intelligence]: Vision and Scene Understanding; 1.3.5 [Computer Graphics]: Computational Geometry and Object Modeling; 1.3.7 [Computer Graphics]: Three Dimensional Graphics and Realism-Animation: 1.6.3 [Simulation and Modeling]: Applications. General Terms: Algorithms, design.b Additional Key Words, and Phrases: flock, herd, school, bird, fish, aggregate motion, particle system, actor, flight, behavioral animation, constraints, path planning. -
Expert Assessment of Stigmergy: a Report for the Department of National Defence
Expert Assessment of Stigmergy: A Report for the Department of National Defence Contract No. W7714-040899/003/SV File No. 011 sv.W7714-040899 Client Reference No.: W7714-4-0899 Requisition No. W7714-040899 Contact Info. Tony White Associate Professor School of Computer Science Room 5302 Herzberg Building Carleton University 1125 Colonel By Drive Ottawa, Ontario K1S 5B6 (Office) 613-520-2600 x2208 (Cell) 613-612-2708 [email protected] http://www.scs.carleton.ca/~arpwhite Expert Assessment of Stigmergy Abstract This report describes the current state of research in the area known as Swarm Intelligence. Swarm Intelligence relies upon stigmergic principles in order to solve complex problems using only simple agents. Swarm Intelligence has been receiving increasing attention over the last 10 years as a result of the acknowledgement of the success of social insect systems in solving complex problems without the need for central control or global information. In swarm- based problem solving, a solution emerges as a result of the collective action of the members of the swarm, often using principles of communication known as stigmergy. The individual behaviours of swarm members do not indicate the nature of the emergent collective behaviour and the solution process is generally very robust to the loss of individual swarm members. This report describes the general principles for swarm-based problem solving, the way in which stigmergy is employed, and presents a number of high level algorithms that have proven utility in solving hard optimization and control problems. Useful tools for the modelling and investigation of swarm-based systems are then briefly described. -
The Emergence of Animal Social Complexity
The Emergence of Animal Social Complexity: theoretical and biobehavioral evidence Bradly Alicea Orthogonal Research (http://orthogonal-research.tumblr.com) Keywords: Social Complexity, Sociogenomics, Neuroendocrinology, Metastability AbstractAbstract ThisThis paper paper will will introduce introduce a a theorytheory ofof emergentemergent animalanimal socialsocial complexitycomplexity usingusing variousvarious results results from from computational computational models models andand empiricalempirical resultsresults.. TheseThese resultsresults willwill bebe organizedorganized into into a avertical vertical model model of of socialsocial complexity.complexity. ThisThis willwill supportsupport thethe perspperspectiveective thatthat social social complexity complexity is isin in essence essence an an emergent emergent phenomenon phenomenon while while helping helping to answerto answer of analysistwo interrelated larger than questions. the individual The first organism.of these involves The second how behavior involves is placingintegrated aggregate at units socialof analysis events larger into thethan context the individual of processes organism. occurring The secondwithin involvesindividual placing organi aggregatesms over timesocial (e.g. events genomic into the and context physiological of processes processes) occurring. withinBy using individual a complex organisms systems over perspective,time (e.g. fivegenomic principles and physiologicalof social complexity processes). can Bybe identified.using a complexThese principles systems