Ant robotics Ant robotics is a special case of swarm robotics. Swarm robots are simple (and hopefully, therefore cheap) robots with limited sensing and computational capabilities. This makes it feasible to deploy teams of swarm robots and take advantage of the resulting fault tolerance and parallelism. Invention[edit] In 1991, American electrical engineer James McLurkin was the first to conceptualize the idea of "robot ants" while working at the MIT Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. Background[edit] Researchers have developed ant robot hardware and software and demonstrated, both in simulation and on physical robots, that single ant robots or teams of ant robots solve robot-navigation tasks (such as path following[4] and terrain coverage[1][6]) robustly and efficiently. See also[edit] References[edit] ^ Jump up to: a b J. External links[edit] Ant robot by Sven KoenigAnt algorithm by Israel Wagner
Distributed Robotics Swarm intelligence Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.[1] The application of swarm principles to robots is called swarm robotics, while 'swarm intelligence' refers to the more general set of algorithms. Example algorithms[edit] Particle swarm optimization[edit] Ant colony optimization[edit] Artificial bee colony algorithm[edit] Artificial bee colony algorithm (ABC) is a meta-heuristic algorithm introduced by Karaboga in 2005,[5] and simulates the foraging behaviour of honey bees. Bacterial colony optimization[edit] The algorithm is based on a lifecycle model that simulates some typical behaviors of E. coli bacteria during their whole lifecycle, including chemotaxis, communication, elimination, reproduction, and migration.[6] Differential evolution[edit]
James McLurkin's Personal Webpage What is a "Swarm"? As robots become more and more useful, multiple robots working together on a single task will become commonplace. Many of the most useful applications of robots are particularly well-suited to this "swarm" approach. Groups of robots can perform these tasks more efficiently, and can perform them in fundamentally different ways than robots working individually. Applications of Robot Swarms There are many applications for swarms of robots. In all these applications, individual robots must work independently, only communicating with other nearby robots. Software from Insects? The main goal of my research is to understand how to use local interactions between nearby robots to produce large-scale group behaviors from the entire swarm. However, developing swarm software from the "top down", i.e. by starting with the group application and trying to determine the individual behaviors that it arises from, is very difficult. The Future Primitive Behaviors orbitRobot (4,027 KB mpg)
Ant colony optimization algorithms Ant behavior was the inspiration for the metaheuristic optimization technique This algorithm is a member of the ant colony algorithms family, in swarm intelligence methods, and it constitutes some metaheuristic optimizations. Initially proposed by Marco Dorigo in 1992 in his PhD thesis,[1][2] the first algorithm was aiming to search for an optimal path in a graph, based on the behavior of ants seeking a path between their colony and a source of food. The original idea has since diversified to solve a wider class of numerical problems, and as a result, several problems have emerged, drawing on various aspects of the behavior of ants. Overview[edit] Summary[edit] In the natural world, ants (initially) wander randomly, and upon finding food return to their colony while laying down pheromone trails. Over time, however, the pheromone trail starts to evaporate, thus reducing its attractive strength. Common extensions[edit] Here are some of most popular variations of ACO Algorithms. to state where to
CS 223A : Introduction to Robotics Winter Quarter 2014 Announcements Schedule Homework Homework Guidelines: Homework is due by 12:50 pm on the due date. Aco branches.svg - Wikipedia, the free encyclopedia From Wikimedia Commons, the free media repository Français :Choix du plus court chemin par une colonie de fourmi Auteur : Johann Dréo (User:Nojhan) Date : 27 mai 2006 Notes : 1) la première fourmi trouve la source de nourriture (F), via un chemin quelconque (a), puis revient au nid (N) en laissant derrière elle une piste de phéromone (b). 2) les fourmis empruntent indifféremment les 4 chemins possibles, mais le renforcement de la piste rend plus attractif le chemin le plus court. 3) les fourmis empruntent le chemin le plus court, les portions longues des autres chemins voient la piste de phéromones s'évaporer. English:Shortest path find by an ant colony Author: Johann Dréo (User:Nojhan) Date: 27 may 2006 Русский:Поиск кратчайшего пути муравьиной колонией Автор: Johann Dréo (User:Nojhan) Дата: 27 мая 2006 Licensing[edit] File history Click on a date/time to view the file as it appeared at that time. You cannot overwrite this file. There are no pages that link to this file. File usage on other wikis
DRL - Main Page Travelling salesman problem The travelling salesman problem (TSP) asks the following question: Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city exactly once and returns to the origin city? It is an NP-hard problem in combinatorial optimization, important in operations research and theoretical computer science. Solution of a travelling salesman problem TSP is a special case of the travelling purchaser problem. In the theory of computational complexity, the decision version of the TSP (where, given a length L, the task is to decide whether the graph has any tour shorter than L) belongs to the class of NP-complete problems. The problem was first formulated in 1930 and is one of the most intensively studied problems in optimization. The TSP has several applications even in its purest formulation, such as planning, logistics, and the manufacture of microchips. History[edit] The origins of the travelling salesman problem are unclear. Richard M.
Knapsack problem Example of a one-dimensional (constraint) knapsack problem: which boxes should be chosen to maximize the amount of money while still keeping the overall weight under or equal to 15 kg? A multiple constrained problem could consider both the weight and volume of the boxes. (Answer: if any number of each box is available, then three yellow boxes and three grey boxes; if only the shown boxes are available, then all but the green box.) The knapsack problem or rucksack problem is a problem in combinatorial optimization: Given a set of items, each with a mass and a value, determine the number of each item to include in a collection so that the total weight is less than or equal to a given limit and the total value is as large as possible. It derives its name from the problem faced by someone who is constrained by a fixed-size knapsack and must fill it with the most valuable items. Applications[edit] Definition[edit] Mathematically the 0-1-knapsack problem can be formulated as: Let there be items, to
Knapsack ants.svg - Wikipedia, the free encyclopedia Summary[edit] Knapsack problem resolved using ants. Ants discover a small drop of honey, they prefer to concentrate their resources on this drop instead of moving to sugar water, in larger quantity but less interesting for the colony. This is similar to the knapsack problem where one tries to find the best items (honey vs water) to carry in a bag with limited capacity (the number of available ants or the size of the colony). Author : DakeSoftware : Inkscape Licensing[edit] Click on a date/time to view the file as it appeared at that time.
Artificial immune system In computer science, artificial immune systems (AIS) are a class of computationally intelligent systems inspired by the principles and processes of the vertebrate immune system. The algorithms typically exploit the immune system's characteristics of learning and memory to solve a problem. Definition[edit] The field of Artificial Immune Systems (AIS) is concerned with abstracting the structure and function of the immune system to computational systems, and investigating the application of these systems towards solving computational problems from mathematics, engineering, and information technology. Artificial Immune Systems (AIS) are adaptive systems, inspired by theoretical immunology and observed immune functions, principles and models, which are applied to problem solving.[1] History[edit] AIS emerged in the mid 1980s with articles authored by Farmer, Packard and Perelson (1986) and Bersini and Varela (1990) on immune networks. Techniques[edit] See also[edit] Notes[edit] References[edit]
Firefly algorithm The firefly algorithm (FA) is a metaheuristic algorithm, inspired by the flashing behaviour of fireflies. The primary purpose for a firefly's flash is to act as a signal system to attract other fireflies. Xin-She Yang formulated this firefly algorithm by assuming:[1] All fireflies are unisexual, so that one firefly will be attracted to all other fireflies;Attractiveness is proportional to their brightness, and for any two fireflies, the less bright one will be attracted by (and thus move to) the brighter one; however, the brightness can decrease as their distance increases;If there are no fireflies brighter than a given firefly, it will move randomly. The brightness should be associated with the objective function. Algorithm description[edit] The pseudo code can be summarized as: Begin 1) Objective function: ; 2) Generate an initial population of fireflies ;. 3) Formulate light intensity so that it is associated with (for example, for maximization problems, or simply and is where The . See also[edit]
MASSIVE (software) MASSIVE (Multiple Agent Simulation System in Virtual Environment) is a high-end computer animation and artificial intelligence software package used for generating crowd-related visual effects for film and television. Massive is a software package developed by Stephen Regelous for the visual effects industry. Its flagship feature is the ability to quickly and easily create thousands (or up to millions with current advances in computer processing power) of agents that all act as individuals as opposed to content creators individually animating or programming the agents by hand. In addition to the artificial intelligence abilities of Massive, there are numerous other features, including cloth simulation, rigid body dynamics and graphics processing unit (GPU) based hardware rendering. Massive has been used in many productions, both commercials and feature-length films, small-scale and large. Some significant examples include: