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In computational intelligence (CI), an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions. Evolution of the population then takes place after the repeated application of the above operators.
Swarm behaviour, or swarming, is a collective behaviour exhibited by entities, particularly animals, of similar size which aggregate together, perhaps milling about the same spot or perhaps moving en masse or migrating in some direction. It is a highly interdisciplinary topic. As a term, swarming is applied particularly to insects, but can also be applied to any other entity or animal that exhibits swarm behaviour. The term flocking or murmuration can refer specifically to swarm behaviour in birds, herding to refer to swarm behaviour in tetrapods, and shoaling or schooling to refer to swarm behaviour in fish. Phytoplankton also gather in huge swarms called blooms, although these organisms are algae and are not self-propelled the way animals are. By extension, the term "swarm" is applied also to inanimate entities which exhibit parallel behaviours, as in a robot swarm, an earthquake swarm, or a swarm of stars.
Boids is an artificial life program, developed by Craig Reynolds in 1986, which simulates the flocking behaviour of birds. His paper on this topic was published in 1987 in the proceedings of the ACM SIGGRAPH conference. The name "boid" corresponds to a shortened version of "bird-oid object", which refers to a bird-like object. "Boid" is also a New York Metropolitan dialect pronunciation for "bird."
Neuromorphic engineering, also known as neuromorphic computing, is the use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system. A neuromorphic computer/chip is any device that uses physical artificial neurons to do computations. In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems. The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors, spintronic memories, threshold switches, and transistors. Training software-based neuromorphic systems of spiking neural networks can be achieved using error backpropagation, e.g., using Python based frameworks such as snnTorch, or using canonical learning rules from the biological learning literature, e.g., using BindsNet.
In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Artificial ants stand for multi-agent methods inspired by the behavior of real ants. The pheromone-based communication of biological ants is often the predominant paradigm used. Combinations of artificial ants and local search algorithms have become a method of choice for numerous optimization tasks involving some sort of graph, e.g., vehicle routing and internet routing.
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.
Simultaneous localization and mapping (SLAM) is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it. While this initially appears to be a chicken-and-egg problem there are several algorithms known for solving it, at least approximately, in tractable time for certain environments. Popular approximate solution methods include the particle filter, extended Kalman filter, covariance intersection, and GraphSLAM. SLAM algorithms are based on concepts in computational geometry and computer vision, and are used in robot navigation, robotic mapping and odometry for virtual reality or augmented reality.
In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, or select a heuristic that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity. Metaheuristics sample a subset of solutions which is otherwise too large to be completely enumerated or otherwise explored. Metaheuristics may make relatively few assumptions about the optimization problem being solved and so may be usable for a variety of problems.
A multi-agent system is a computerized system composed of multiple interacting intelligent agents. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. Intelligence may include methodic, functional, procedural approaches, algorithmic search or reinforcement learning.
Swarm robotics is an approach to the coordination of multiple robots as a system which consist of large numbers of mostly simple physical robots. It is supposed that a desired collective behavior emerges from the interactions between the robots and interactions of robots with the environment. This approach emerged on the field of artificial swarm intelligence, as well as the biological studies of insects, ants and other fields in nature, where swarm behaviour occurs.
A decentralised system in systems theory is a system in which lower level components operate on local information to accomplish global goals. The global pattern of behaviour is an emergent property of dynamical mechanisms that act upon local components, such as indirect communication, rather than the result of a central ordering influence of a centralised system.
Gregory Dudek is a chaired professor of computer science at McGill University, was the Director of the McGill Center for Intelligent Machines from 2004 to 2007, and was the Director of the McGill University School of Computer Science from 2008 to 2016. He served as the Scientific Director of the NSERC Canadian Field Robotics Network from 2012 to 2018. He became Scientific Director and Lead Investigatior or it ssuccessor the NSERC Canadian Robotics Network. In 2018, Samsung announced that he would become a VP Research and Lead their new Samsung AI Center in Montreal (SAIC-Montreal). He is the son of poet Louis Dudek, he was made a Dawson Scholar of that university and subsequently James McGill Chair, and directs the mobile robotics laboratory there. He has written over 300 refereed articles on computer vision and robotics, and is co-author of the book Computational Principles of Mobile Robotics which is used to teach robotics at a number of universities [1].
A rapidly exploring random tree (RRT) is an algorithm designed to efficiently search nonconvex, high-dimensional spaces by randomly building a space-filling tree. The tree is constructed incrementally from samples drawn randomly from the search space and is inherently biased to grow towards large unsearched areas of the problem. RRTs were developed by Steven M. LaValle and James J. Kuffner Jr. They easily handle problems with obstacles and differential constraints and have been widely used in autonomous robotic motion planning.
Any-angle path planning algorithms are a subset of pathfinding algorithms that search for a path between two points in space and allow the turns in the path to have any angle. The result is a path that goes directly toward the goal and has relatively few turns. Other pathfinding algorithms such as A* constrain the paths to a grid, which produces jagged, indirect paths.
Consensus dynamics or agreement dynamics is an area of research lying at the intersection of systems theory and graph theory. A major topic of investigation is the agreement or consensus problem in multi-agent systems that concerns processes by which a collection of interacting agents achieve a common goal. Networks of agents that exchange information to reach consensus include: physiological systems, gene networks, large-scale energy systems and fleets of vehicles on land, in the air or in space. The agreement protocol or consensus protocol is an unforced dynamical system that is governed by the interconnection topology and the initial condition for each agent. Other problems are the rendezvous problem, synchronization, flocking, formation control. One solution paradigm is distributed constraint reasoning.
Swarm robotic platforms apply swarm robotics in multi-robot collaboration. They take inspiration from nature. The main goal is to control a large number of robots to accomplish a common task/problem. Hardware limitation and cost of robot platforms limit current research in swarm robotics to mostly performed by simulation software. On the other hand, simulation of swarm scenarios that needs large numbers of agents is extremely complex and often inaccurate due to poor modelling of external conditions and limitation of computation.
This is a chronological table of metaheuristic algorithms that only contains fundamental algorithms. Hybrid algorithms and multi-objective algorithms are not listed in the table below.
Alois Christian Knoll is German computer scientist and professor at the TUM Department of Informatics at the Technical University of Munich (TUM). He is head of the Chair of Robotics, Artificial Intelligence and Real-Time Systems and known for seminal contributions to Human–Robot-Interaction, Neurorobotics and Autonomous Systems.
References
- 1 2 Svennebring, Jonas; Koenig, Sven (2004), "Building terrain-covering ant robots" (PDF), Autonomous Robots, Kluwer Academic Publishers, vol. 16, no. 3, pp. 313–332, CiteSeerX 10.1.1.380.355 , doi:10.1023/B:AURO.0000025793.46961.f6, S2CID 11666383, archived from the original on 2020-07-30, retrieved 2021-11-13
- ↑ Batalin, Maxim; Sukhatme, Guarav (2003-09-14), "Efficient exploration without localization" (PDF), Proceedings of the International Conference on Robotics and Automation, IEEE, pp. 2714–2719, doi:10.1109/ROBOT.2003.1242003, ISBN 0-7803-7736-2, S2CID 6688649, archived (PDF) from the original on 2020-07-28, retrieved 2021-11-13
- ↑ Russell, R. Andrew (1997-04-25), "Heat trails as short-lived navigational markers for mobile robots", Proceedings of the International Conference on Robotics and Automation, IEEE, vol. 4, pp. 3534–3539, doi:10.1109/ROBOT.1997.606882, ISBN 0-7803-3612-7, S2CID 206539010, archived from the original on 2020-07-26, retrieved 2021-11-13
- ↑ Sharpe, Titus; Webb, Barbara (1998), "Simulated and situated models of chemical trail following in ants", Proceedings of the International Conference on Simulation of Adaptive Behavior, The MIT Press, pp. 195–204, doi:10.7551/mitpress/3119.003.0031, ISBN 9780262291385 , retrieved 2021-11-13
{{citation}}: CS1 maint: url-status (link) - ↑ Vaughan, Richard T.; Støy, Kasper; Sukhatme, Gaurav S.; Matarić, Maja J. (2002), "LOST: Localization-space trails for robot teams" (PDF), IEEE Transactions on Robotics and Automation, IEEE, 18 (5): 796–812, doi:10.1109/TRA.2002.803459, archived (PDF) from the original on 2021-11-13, retrieved 2021-11-13
- ↑ "James McLurkin". Massachusetts Institute of Technology. Archived from the original on 2003-04-15.
- ↑ Wagner, Israel A.; Bruckstein, Alfred M. (2001-05-02), "From Ants to A(ge)nts: A Special Issue on Ant-Robotics", Annals of Mathematics and Artificial Intelligence, IEEE, 31 (1–4): 1–5, doi:10.1023/A:1016666118983, S2CID 28530209, archived from the original on 2018-06-03, retrieved 2021-11-13
