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Swarm Robotics Applications

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  1. Swarm Robotics For Agricultural Applications
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  • Swarm robotics has many applications in numerous domains. Swarm robots can be deployed in areas which are spread in space such as environmental monitoring of lake. The distributed sensing ability of swarm robotic system can provide surveillance for immediate detection of hazardous events, such as the accidental leakage of a chemical.
  • Swarm Intelligence and Its Applications in Swarm Robotics ALEKSANDARJEVTIC´ UniversidadPolitecnicadeMadrid´ E.T.S.I.Telecomunicacion´ SPAIN DIEGOANDINA.
  • Swarm robotics is a field of multi-robotics in which a large number of robots are coordinated in a distributed and a decentralized way. It's based upon the use of local rules, small simple robots inspired by the collective behavior of social insects so that a large number of simple robots can outperform a complex task in a more efficient way.

Swarm Robotics Scalability Synchronous Tasks Resilience to Environments Current Developments Future Applications Berman, S., Halasz, A., Kumar, V., & Pratt, S. Bio-inspired group behaviors for the deployment of a swarm of robots to multiple destinations. The potential applications of swarm robotics include the tasks that demand the miniaturization, like distributed sensing tasks in micro machinery or the human body. On the other hand, the swarm robotics can be suited to the tasks that demand the cheap designs, such as. Beside being relevant to engineering applications, swarm robotics is also a valuable scientific tool. Indeed, several models of natural swarm intelligence systems have been refined and validated using robot swarms. For example, Garnier et al. (2005) validated the model of a collective decision-making behavior in cockroaches using robot swarms.

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]

SI systems consist typically of a population of simple agents or boids interacting locally with one another and with their environment. The inspiration often comes from nature, especially biological systems. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of 'intelligent' global behavior, unknown to the individual agents. Examples of swarm intelligence in natural systems include ant colonies, bird flocking, hawks hunting, animal herding, bacterial growth, fish schooling and microbial intelligence.

The application of swarm principles to robots is called swarm robotics, while 'swarm intelligence' refers to the more general set of algorithms. https://rentatree.tistory.com/12. 'Swarm prediction' has been used in the context of forecasting problems. Similar approaches to those proposed for swarm robotics are considered for genetically modified organisms in synthetic collective intelligence. [2]

  • 1Models of swarm behavior
  • 2Metaheuristics
  • 3Applications

Models of swarm behavior[edit]

Boids (Reynolds 1987)[edit]

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 ACMSIGGRAPH conference.[3]The name 'boid' corresponds to a shortened version of 'bird-oid object', which refers to a bird-like object.[4]

As with most artificial life simulations, Boids is an example of emergent behavior; that is, the complexity of Boids arises from the interaction of individual agents (the boids, in this case) adhering to a set of simple rules. The rules applied in the simplest Boids world are as follows:

  • separation: steer to avoid crowding local flockmates
  • alignment: steer towards the average heading of local flockmates
  • cohesion: steer to move toward the average position (center of mass) of local flockmates

More complex rules can be added, such as obstacle avoidance and goal seeking.

Self-propelled particles (Vicsek et al. 1995)[edit]

Self-propelled particles (SPP), also referred to as the Vicsek model, was introduced in 1995 by Vicseket al.[5] as a special case of the boids model introduced in 1986 by Reynolds.[6] A swarm is modelled in SPP by a collection of particles that move with a constant speed but respond to a random perturbation by adopting at each time increment the average direction of motion of the other particles in their local neighbourhood.[7] SPP models predict that swarming animals share certain properties at the group level, regardless of the type of animals in the swarm.[8] Swarming systems give rise to emergent behaviours which occur at many different scales, some of which are turning out to be both universal and robust. It has become a challenge in theoretical physics to find minimal statistical models that capture these behaviours.[9][10][11]

Metaheuristics[edit]

Evolutionary algorithms (EA), particle swarm optimization (PSO), Differential Evolution (DE), ant colony optimization (ACO) and their variants dominate the field of nature-inspired metaheuristics.[12] This list includes algorithms published up to circa the year 2000. A large number of more recent metaphor-inspired metaheuristics have started to attract criticism in the research community for hiding their lack of novelty behind an elaborate metaphor. For algorithms published since that time, see List of metaphor-based metaheuristics.

Stochastic diffusion search (Bishop 1989)[edit]

First published in 1989 Stochastic diffusion search (SDS)[13][14] was the first Swarm Intelligence metaheuristic. SDS is an agent-based probabilistic global search and optimization technique best suited to problems where the objective function can be decomposed into multiple independent partial-functions. Each agent maintains a hypothesis which is iteratively tested by evaluating a randomly selected partial objective function parameterised by the agent's current hypothesis. In the standard version of SDS such partial function evaluations are binary, resulting in each agent becoming active or inactive. Information on hypotheses is diffused across the population via inter-agent communication. Unlike the stigmergic communication used in ACO, in SDS agents communicate hypotheses via a one-to-one communication strategy analogous to the tandem running procedure observed in Leptothorax acervorum.[15] A positive feedback mechanism ensures that, over time, a population of agents stabilise around the global-best solution. SDS is both an efficient and robust global search and optimisation algorithm, which has been extensively mathematically described.[16][17][18] Recent work has involved merging the global search properties of SDS with other swarm intelligence algorithms.[19][20]

Ant colony optimization (Dorigo 1992)[edit]

Ant colony optimization (ACO), introduced by Dorigo in his doctoral dissertation, is a class of optimizationalgorithms modeled on the actions of an ant colony. ACO is a probabilistic technique useful in problems that deal with finding better paths through graphs. Artificial 'ants'—simulation agents—locate optimal solutions by moving through a parameter space representing all possible solutions. Natural ants lay down pheromones directing each other to resources while exploring their environment. The simulated 'ants' similarly record their positions and the quality of their solutions, so that in later simulation iterations more ants locate for better solutions.[21]

Particle swarm optimization (Kennedy, Eberhart & Shi 1995)[edit]

Particle swarm optimization (PSO) is a global optimization algorithm for dealing with problems in which a best solution can be represented as a point or surface in an n-dimensional space. Hypotheses are plotted in this space and seeded with an initial velocity, as well as a communication channel between the particles.[22][23] Particles then move through the solution space, and are evaluated according to some fitness criterion after each timestep. Over time, particles are accelerated towards those particles within their communication grouping which have better fitness values. The main advantage of such an approach over other global minimization strategies such as simulated annealing is that the large number of members that make up the particle swarm make the technique impressively resilient to the problem of local minima.

Applications[edit]

Swarm Intelligence-based techniques can be used in a number of applications. The U.S. military is investigating swarm techniques for controlling unmanned vehicles. The European Space Agency is thinking about an orbital swarm for self-assembly and interferometry. NASA is investigating the use of swarm technology for planetary mapping. A 1992 paper by M. Anthony Lewis and George A. Bekey discusses the possibility of using swarm intelligence to control nanobots within the body for the purpose of killing cancer tumors.[24] Conversely al-Rifaie and Aber have used stochastic diffusion search to help locate tumours.[25][26] Swarm intelligence has also been applied for data mining.[27]

Ant-based routing[edit]

The use of swarm intelligence in telecommunication networks has also been researched, in the form of ant-based routing. This was pioneered separately by Dorigo et al. and Hewlett Packard in the mid-1990s, with a number of variants are existed. Basically, this uses a probabilistic routing table rewarding/reinforcing the route successfully traversed by each 'ant' (a small control packet) which flood the network. Reinforcement of the route in the forwards, reverse direction and both simultaneously have been researched: backwards reinforcement requires a symmetric network and couples the two directions together; forwards reinforcement rewards a route before the outcome is known (but then one would pay for the cinema before one knows how good the film is). As the system behaves stochastically and is therefore lacking repeatability, there are large hurdles to commercial deployment. Mobile media and new technologies have the potential to change the threshold for collective action due to swarm intelligence (Rheingold: 2002, P175).

The location of transmission infrastructure for wireless communication networks is an important engineering problem involving competing objectives. A minimal selection of locations (or sites) are required subject to providing adequate area coverage for users. A very different-ant inspired swarm intelligence algorithm, stochastic diffusion search (SDS), has been successfully used to provide a general model for this problem, related to circle packing and set covering. It has been shown that the SDS can be applied to identify suitable solutions even for large problem instances.[28]

Airlines have also used ant-based routing in assigning aircraft arrivals to airport gates. At Southwest Airlines a software program uses swarm theory, or swarm intelligence—the idea that a colony of ants works better than one alone. Each pilot acts like an ant searching for the best airport gate. 'The pilot learns from his experience what's the best for him, and it turns out that that's the best solution for the airline,' Douglas A. Lawson explains. As a result, the 'colony' of pilots always go to gates they can arrive at and depart from quickly. The program can even alert a pilot of plane back-ups before they happen. 'We can anticipate that it's going to happen, so we'll have a gate available,' Lawson says.[29]

Crowd simulation[edit]

Artists are using swarm technology as a means of creating complex interactive systems or simulating crowds.

Stanley and Stella in: Breaking the Ice was the first movie to make use of swarm technology for rendering, realistically depicting the movements of groups of fish and birds using the Boids system. Tim Burton's Batman Returns also made use of swarm technology for showing the movements of a group of bats. The Lord of the Rings film trilogy made use of similar technology, known as Massive, during battle scenes. Swarm technology is particularly attractive because it is cheap, robust, and simple.

Airlines have used swarm theory to simulate passengers boarding a plane. Southwest Airlines researcher Douglas A. Lawson used an ant-based computer simulation employing only six interaction rules to evaluate boarding times using various boarding methods.(Miller, 2010, xii-xviii).[30]

Online

Human swarming[edit]

'Human Swarm' - this animated GIF shows a group of networked human participants, thinking together as a real-time system (i.e. a Hive Mind) moderated by swarming algorithms.

Enabled by mediating software such as the SWARM platform (formally unu) from Unanimous A.I., networks of distributed users can be organized into 'human swarms' through the implementation of real-time closed-loop control systems.[31][32][33][32] As published by Rosenberg (2015), such real-time systems enable groups of human participants to behave as a unified collective intelligence that works as a single entity to make predictions, answer questions, and evoke opinions.[34] Such systems, also referred to as 'Artificial Swarm Intelligence' (or the brand name Swarm AI) have been shown to significantly amplify human intelligence,[35][36][37] resulting in a string of high-profile predictions of extreme accuracy.[38][39][40][41][32][42] Academic testing shows that human swarms can out-predict individuals across a variety of real-world projections.[43][44][33][45][46] Famously, human swarming was used to correctly predict the Kentucky Derby Superfecta, against 541 to 1 odds, in response to a challenge from reporters.[47]

Medical Use of Human Swarming—in 2018, Stanford University School of Medicine and Unanimous AI published studies showing that groups of human doctors, when connected together by real-time swarming algorithms, could diagnose medical conditions with substantially higher accuracy than individual doctors or groups of doctors working together using traditional crowd-sourcing methods. In one such study, swarms of human radiologists connected together using the SWARM platform were tasked with diagnosing chest x-rays and demonstrated a 33% reduction in diagnostic errors as compared to the traditional human methods, and a 22% improvement over traditional machine-learning.[48][49][50][51][52]

Swarm grammars[edit]

Swarm grammars are swarms of stochastic grammars that can be evolved to describe complex properties such as found in art and architecture.[53] These grammars interact as agents behaving according to rules of swarm intelligence. Such behavior can also suggest deep learning algorithms, in particular when mapping of such swarms to neural circuits is considered.[54]

Swarmic art[edit]

In a series of works al-Rifaie et al.[55] have successfully used two swarm intelligence algorithms—one mimicking the behaviour of one species of ants (Leptothorax acervorum) foraging (stochastic diffusion search, SDS) and the other algorithm mimicking the behaviour of birds flocking (particle swarm optimization, PSO)—to describe a novel integration strategy exploiting the local search properties of the PSO with global SDS behaviour. The resulting hybrid algorithm is used to sketch novel drawings of an input image, exploiting an artistic tension between the local behaviour of the 'birds flocking'—as they seek to follow the input sketch—and the global behaviour of the 'ants foraging'—as they seek to encourage the flock to explore novel regions of the canvas. The 'creativity' of this hybrid swarm system has been analysed under the philosophical light of the 'rhizome' in the context of Deleuze's 'Orchid and Wasp' metaphor.[56]

In a more recent work of al-Rifaie et al., 'Swarmic Sketches and Attention Mechanism',[57] introduces a novel approach deploying the mechanism of 'attention' by adapting SDS to selectively attend to detailed areas of a digital canvas. Once the attention of the swarm is drawn to a certain line within the canvas, the capability of PSO is used to produce a 'swarmic sketch' of the attended line. The swarms move throughout the digital canvas in an attempt to satisfy their dynamic roles—attention to areas with more details—associated to them via their fitness function. Having associated the rendering process with the concepts of attention, the performance of the participating swarms creates a unique, non-identical sketch each time the 'artist' swarms embark on interpreting the input line drawings. In other works while PSO is responsible for the sketching process, SDS controls the attention of the swarm.

In a similar work, 'Swarmic Paintings and Colour Attention',[58] non-photorealistic images are produced using SDS algorithm which, in the context of this work, is responsible for colour attention.

The 'computational creativity' of the above-mentioned systems are discussed in[55][59][60] through the two prerequisites of creativity (i.e. freedom and constraints) within the swarm intelligence's two infamous phases of exploration and exploitation.

Michael Theodore and Nikolaus Correll use swarm intelligent art installation to explore what it takes to have engineered systems to appear lifelike.[61]

Notable researchers[edit]

See also[edit]

References[edit]

  1. ^Beni, G., Wang, J. (1993). 'Swarm Intelligence in Cellular Robotic Systems'. Proceed. NATO Advanced Workshop on Robots and Biological Systems, Tuscany, Italy, June 26–30 (1989). pp. 703–712. doi:10.1007/978-3-642-58069-7_38. ISBN978-3-642-63461-1.CS1 maint: multiple names: authors list (link)
  2. ^Solé R, Rodriguez-Amor D, Duran-Nebreda S, Conde-Pueyo N, Carbonell-Ballestero M, Montañez R (October 2016). 'Synthetic Collective Intelligence'. BioSystems. 148: 47–61. doi:10.1016/j.biosystems.2016.01.002.
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  34. ^http://sites.lsa.umich.edu/collectiveintelligence/wp-content/uploads/sites/176/2015/05/Rosenberg-CI-2015-Abstract.pdf
  35. ^Metcalf, Lynn; Askay, David A.; Rosenberg, Louis B. (2019-07-17). 'Keeping Humans in the Loop: Pooling Knowledge through Artificial Swarm Intelligence to Improve Business Decision Making'. California Management Review: 000812561986225. doi:10.1177/0008125619862256. ISSN0008-1256.
  36. ^Willcox, Gregg; Rosenberg, Louis; Askay, David; Metcalf, Lynn; Harris, Erick; Domnauer, Colin (2020). Arai, Kohei; Bhatia, Rahul (eds.). 'Artificial Swarming Shown to Amplify Accuracy of Group Decisions in Subjective Judgment Tasks'. Advances in Information and Communication. Lecture Notes in Networks and Systems. Springer International Publishing: 373–383. doi:10.1007/978-3-030-12385-7_29. ISBN9783030123857.
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  55. ^ abal-Rifaie, MM; Bishop, J.M.; Caines, S. (2012). 'Creativity and Autonomy in Swarm Intelligence Systems'(PDF). Cognitive Computation. 4 (3): 320–331. doi:10.1007/s12559-012-9130-y.
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  57. ^Al-Rifaie, Mohammad Majid; Bishop, John Mark (2013). 'Swarmic Sketches and Attention Mechanism'(PDF). Evolutionary and Biologically Inspired Music, Sound, Art and Design. Lecture Notes in Computer Science. 7834. pp. 85–96. doi:10.1007/978-3-642-36955-1_8. ISBN978-3-642-36954-4.
  58. ^al-Rifaie, Mohammad Majid, and John Mark Bishop. 'Swarmic paintings and colour attention'. Evolutionary and Biologically Inspired Music, Sound, Art and Design. Springer Berlin Heidelberg, 2013. 97-108.
  59. ^al-Rifaie, Mohammad Majid, Mark JM Bishop, and Ahmed Aber. 'Creative or Not? Birds and Ants Draw with Muscle.' Proceedings of AISB'11 Computing and Philosophy (2011): 23-30.
  60. ^al-Rifaie MM, Bishop M (2013) Swarm intelligence and weak artificial creativity. In: The Association for the Advancement of Artificial Intelligence (AAAI) 2013: Spring Symposium, Stanford University, Palo Alto, California, U.S.A., pp 14–19
  61. ^N. Correll, N. Farrow, K. Sugawara, M. Theodore (2013): The Swarm Wall: Toward Life’s Uncanny Valley. In: K. Goldberg, H. Knight, P. Salvini (Ed.): IEEE International Conference on Robotics and Automation, Workshop on Art and Robotics: Freud's Unheimlich and the Uncanny Valley.

Further reading[edit]

  • Bonabeau, Eric; Dorigo, Marco; Theraulaz, Guy (1999). Swarm Intelligence: From Natural to Artificial Systems. ISBN978-0-19-513159-8.
  • Kennedy, James; Eberhart, Russell C. (2001-04-09). Swarm Intelligence. ISBN978-1-55860-595-4.
  • Engelbrecht, Andries (2005-12-16). Fundamentals of Computational Swarm Intelligence. Wiley & Sons. ISBN978-0-470-09191-3.

Swarm Robotics For Agricultural Applications

External links[edit]

  • Marco Dorigo and Mauro Birattari (2007). 'Swarm intelligence' in Scholarpedia
  • Antoinette Brown. Swarm Intelligence
Retrieved from 'https://en.wikipedia.org/w/index.php?title=Swarm_intelligence&oldid=918582342'
Swarm of open-source Jasmine micro-robots recharging themselves
A team of iRobot Createrobots at the Georgia Institute of Technology

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.

  • 2Goals and applications

Definition[edit]

The research of swarm robotics is to study the design of robots, their physical body and their controlling behaviours. It is inspired but not limited by[1] the emergent behaviour observed in social insects, called swarm intelligence. Relatively simple individual rules can produce a large set of complex swarm behaviours. A key-component is the communication between the members of the group that build a system of constant feedback. The swarm behaviour involves constant change of individuals in cooperation with others, as well as the behaviour of the whole group.

Swarm Robotics Applications Free

Unlike distributed robotic systems in general, swarm robotics emphasizes a large number of robots, and promotes scalability, for instance by using only local communication.[2] That local communication for example can be achieved by wireless transmission systems, like radio frequency or infrared.[3]

Goals and applications[edit]

Miniaturization and cost are key factors in swarm robotics. These are the constraints in building large groups of robots; therefore the simplicity of the individual team member should be emphasized. This should motivate a swarm-intelligent approach to achieve meaningful behavior at swarm-level, instead of the individual level.
Much research has been directed at this goal of simplicity at the individual robot level. Being able to use actual hardware in research of Swarm Robotics rather than simulations allows researchers to encounter and resolve many more issues and broaden the scope of Swarm Research. Thus, development of simple robots for Swarm intelligence research is a very important aspect of the field. The goals include keeping the cost of individual robots low to allow scalability, making each member of the swarm less demanding of resources and more power/energy efficient.

One such swarm system is the LIBOT Robotic System[4] that involves a low cost robot built for outdoor swarm robotics. The robots are also made with provisions for indoor use via Wi-Fi, since the GPS sensors provide poor communication inside buildings. Another such attempt is the micro robot (Colias),[5] built in the Computer Intelligence Lab at the University of Lincoln, UK. This micro robot is built on a 4 cm circular chassis and is low-cost and open platform for use in a variety of Swarm Robotics applications.

Applications[edit]

Potential applications for swarm robotics are many. They include tasks that demand miniaturization (nanorobotics, microbotics), like distributed sensing tasks in micromachinery or the human body. One of the most promising uses of swarm robotics is in disaster rescue missions. Swarms of robots of different sizes could be sent to places rescue workers can't reach safely, to detect the presence of life via infra-red sensors. On the other hand, swarm robotics can be suited to tasks that demand cheap designs, for instance mining or agricultural foraging tasks.

More controversially, swarms of military robots can form an autonomous army. U.S. Naval forces have tested a swarm of autonomous boats that can steer and take offensive actions by themselves. The boats are unmanned and can be fitted with any kind of kit to deter and destroy enemy vessels.[6]

During the Syrian Civil War, Russian forces in the region reported attacks on their main air force base in the country by swarms of fixed-wing drones loaded with explosives.[7]

Most efforts have focused on relatively small groups of machines. However, a swarm consisting of 1,024 individual robots was demonstrated by Harvard in 2014, the largest to date.[8]

Another large set of applications may be solved using swarms of micro air vehicles, which are also broadly investigated nowadays. In comparison with the pioneering studies of swarms of flying robots using precise motion capture systems in laboratory conditions,[9] current systems such as Shooting Star can control teams of hundreds of micro aerial vehicles in outdoor environment[10] using GNSS systems (such as GPS) or even stabilize them using onboard localization systems[11] where GPS is unavailable.[12][13] Swarms of micro aerial vehicles have been already tested in tasks of autonomous surveillance,[14] plume tracking,[15] and reconnaissance in a compact phalanx.[16] Numerous works on cooperative swarms of unmanned ground and aerial vehicles have been conducted with target applications of cooperative environment monitoring,[17] convoy protection,[18] and moving target localization and tracking.[19]

Swarm Robotics Applications Download

Drone displays[edit]

A drone display commonly uses multiple, lighted drones at night for an artistic display or advertising.

In popular culture[edit]

A major subplot of Disney's Big Hero 6 involved the use of swarms of microbots to form structures.

Swarm robotics is used in the Tamil film, Enthiran, and its sequel 2.0.

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See also[edit]

  • Unmanned aerial vehicle/Quadcopter

References[edit]

  1. ^Hunt, Edmund R. (2019-03-27). 'The social animals that are inspiring new behaviours for robot swarms'. The Conversation. Retrieved 2019-03-28.
  2. ^Hamann, H. (2018). Swarm Robotics: A Formal Approach. New York: Springer International Publishing. ISBN978-3-319-74528-2.
  3. ^N. Correll, D. Rus. Architectures and control of networked robotic systems. In: Serge Kernbach (Ed.): Handbook of Collective Robotics, pp. 81-104, Pan Stanford, Singapore, 2013.
  4. ^Zahugi, Emaad Mohamed H.; Shabani, Ahmed M.; Prasad, T. V. (2012), 'Libot: Design of a low cost mobile robot for outdoor swarm robotics', 2012 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), pp. 342–347, doi:10.1109/CYBER.2012.6392577, ISBN978-1-4673-1421-3
  5. ^Arvin, F.; Murray, J.C.; Licheng Shi; Chun Zhang; Shigang Yue, 'Development of an autonomous micro robot for swarm robotics,' Mechatronics and Automation (ICMA), 2014 IEEE International Conference on , vol., no., pp.635,640, 3-6 Aug. 2014 doi: 10.1109/ICMA.2014.6885771
  6. ^Lendon, Brad. 'U.S. Navy could 'swarm' foes with robot boats'. CNN.
  7. ^Madrigal, Alexis C. (2018-03-07). 'Drone Swarms Are Going to Be Terrifying and Hard to Stop'. The Atlantic. Retrieved 2019-03-07.
  8. ^'A self-organizing thousand-robot swarm'. Harvard. 14 August 2014. Retrieved 16 August 2014.
  9. ^Kushleyev, A.; Mellinger, D.; Powers, C.; Kumar, V., 'Towards a swarm of agile micro quadrotors' Autonomous Robots, Volume 35, Issue 4, pp 287-300, November 2013
  10. ^Vasarhelyi, G.; Virágh, C.; Tarcai, N.; Somorjai, G.; Vicsek, T. Outdoor flocking and formation flight with autonomous aerial robots. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), 2014
  11. ^Faigl, J.; Krajnik, T.; Chudoba, J.; Preucil, L.; Saska, M. Low-Cost Embedded System for Relative Localization in Robotic Swarms. In ICRA2013: Proceedings of 2013 IEEE International Conference on Robotics and Automation. 2013.
  12. ^Saska, M.; Vakula, J.; Preucil, L. Swarms of Micro Aerial Vehicles Stabilized Under a Visual Relative Localization. In ICRA2014: Proceedings of 2014 IEEE International Conference on Robotics and Automation. 2014.
  13. ^Saska, M. MAV-swarms: unmanned aerial vehicles stabilized along a given path using onboard relative localization. In Proceedings of 2015 International Conference on Unmanned Aircraft Systems (ICUAS). 2015
  14. ^Saska, M.; Chudoba, J.; Preucil, L.; Thomas, J.; Loianno, G.; Tresnak, A.; Vonasek, V.; Kumar, V. Autonomous Deployment of Swarms of Micro-Aerial Vehicles in Cooperative Surveillance. In Proceedings of 2014 International Conference on Unmanned Aircraft Systems (ICUAS). 2014.
  15. ^Saska, M.; Langr J.; L. Preucil. Plume Tracking by a Self-stabilized Group of Micro Aerial Vehicles. In Modelling and Simulation for Autonomous Systems, 2014.
  16. ^Saska, M.; Kasl, Z.; Preucil, L. Motion Planning and Control of Formations of Micro Aerial Vehicles. In Proceedings of the 19th World Congress of the International Federation of Automatic Control. 2014.
  17. ^Saska, M.; Vonasek, V.; Krajnik, T.; Preucil, L. Coordination and Navigation of Heterogeneous UAVs-UGVs Teams Localized by a Hawk-Eye Approach. In Proceedings of 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2012.
  18. ^Saska, M.; Vonasek, V.; Krajnik, T.; Preucil, L. Coordination and Navigation of Heterogeneous MAV–UGV Formations Localized by a ‘hawk-eye’-like Approach Under a Model Predictive Control Scheme. International Journal of Robotics Research 33(10):1393–1412, September 2014.
  19. ^Kwon, H; Pack, D. J. A Robust Mobile Target Localization Method for Cooperative Unmanned Aerial Vehicles Using Sensor Fusion Quality. Journal of Intelligent and Robotic Systems, Volume 65, Issue 1, pp 479-493, January 2012.

External links[edit]

Retrieved from 'https://en.wikipedia.org/w/index.php?title=Swarm_robotics&oldid=915943165'

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