C.3 Problem Formulation

C.3.1)

Communication and Problem Solving in Large Groups of Insects

Insects can communicate in large groups to accomplish large tasks that they would be incapable of completing alone. For example Hölldobler and Wilson [1] have discovered that ants have 12 different functional categories of communication, which they use for many crucial activities like finding food and carrying large items. Similarly, when honeybee swarms move to a new home, only scouts, which make up 5% of the swarm, know the direction to fly, and yet the honeybees can communicate quickly enough to move directly to their destination. Source [2] finds that this is because scouts fly quickly throughout the swarm to indicate the correct direction. Modeling the way ants track and remove food, presents very different applications that modeling the way swarms find a new hive, but both models can have interesting applications in robotics.

Applications of Robotic Group Communication

Robots with the ability to communicate in large groups present new opportunities across different fields in robotics. Source [3], a review of current research in swarm robotics and look into the future of the field, suggests three main areas of application. The first potential application is traffic control; animals can navigate around each other without collision. This behavior could lead to new, collision-avoidant, safety measures in cars. The second application is "box-pushing," the ability for ants and other animals to cooperate to manipulate large objects, which has potential to create new transportation methods. The final potential application is in foraging, or picking up objects scattered across an environment. This could be used for toxic waste clean up, harvesting, or new search and rescue solutions.

C.3.2)

Capabilities of Existing Technology

Paper [4] examines the advantage of using a swarm-based system with respect to a local navigation problem for off-road robotic systems. These systems were based on vision-based sensing and model ant-like cooperative behavior. Ultimately the results are as follows:

…the ability of the model to robustly control the robot on a local navigation task, with less than 1% of the robot’s visual input being analysed. Hence, with this system the computational cost of perception is considerably reduced, thus fostering robot miniaturisation and energetic efficiency.

Another simulation, documented in [14], has two sub-swarms of robots collaborating to find a path within a cluttered environment. This relates directly to a search and rescue scenario, but uses two different types of robots, flying and wheeled, in order to accomplish the task. Finally, source [10] examines a robotic model based off of the way ants change their tasks in order to meet the changing needs necessary to accomplish a given task. Ultimately, this study finds that:

…the incorporation of task switching mechanisms in specialized groups of robots improves the foraging efficiency and swarm significantly.

C.3.3)

Desirability of Bioinspiration

Source [9] offers a collection of mechanism of group decision amongst some species of ants and bees in the selection of the nest. This article gives specific examples on how social organisms decide between multiple valid candidates to select the best solution. This is relevant to our problem statement because any sort of swarming and cooperation amongst autonomous robots must be mediated by a decision making method. This being a bio-inspired course, one approach to determining this decision making methods would be to examine existing biological systems with behavior similar to those desired. Article [15] categorizes three different forms of insect communication that may be borrowed for robotics:

Indirect or stigmergic communication. A form of communication that takes place through the environment, as a result of the actions performed by some individuals, which indirectly influence someone else’s behaviour (e.g. pheromone trails).

Direct interaction. A form of communication that implies a non-mediated transmission of information, as a result of the actions performed by some individ- uals, which directly influence someone else’s behaviour (e.g. antennation, mandibular pulling).

Direct communication. A form of communication that implies a non-mediated transmission of information, without the need of any physical interaction (e.g. the waggle dance, stridulations).

C.3.4)

The Idea

In disaster situations, search and rescue situations are dangerous. Compounded by the frequent lack of human and material resources, robotic search and rescue teams could be a great help. A team of off-road robots could be equipped with infrared, sound, and visual sensors which allows it to search for survivors, supplies, or key specified items. For the robot team to be fully autonomous, it would need certain decision making capability independent of human interaction. Ant-based foraging technique can be utilized on a smaller scale to dictate the actions of larger robots. There would be similar negative feedback to ensure that the robots remain dispersed but gather when needed, find their target, and complete tasks such as food and water delivery and notification of necessary authorities.

C.3.5)

Refutability

The project proposed can be refuted or supported through testing through models. Computer models mimicking basic disaster situations will be created and the different swarm behaviors of a team of a set number of robots, will be tested in this environment. The behavioral success will be dependent on a few basic survival parameters of the "trapped" humans in the simulation or by comparisons against existing swarm behaviors.

C.3.6)

Necessary Means

The first step is to develop a testing environment for our robot behavior algorithms. The environment would have randomly generated disaster sites and certain tasks and instructions embedded. Models of robots will be released into this environment and made to follow certain behavioral algorithms. These will then be optimized based on different bio-inspired behaviors. Different models, predominantly ant behavior, but possibly bee and bacterial behavior will also be considered. Success will be judged by certain parameters such as efficiency and speed of task completion, match with desired behavior and so on.

C.3.7)

Robotics Paper

The paper [4] was chosen based on its relevance to the problem stated. The SJR value on Scopus was rather low (0.079) indicating that the source, Swarm Intelligence, is limited in its prestige. However, this is a rather new journal, dating back only to 2008 and may have not had the time to accumulate prestige. The SNIP was 1.390 indicating that while the journal has not yet gained acknowledgement, the papers have shown impact on the scientific community. An additional consideration when evaluating this journal is that it is a small journal with very few documents in a small subject area. For this particular paper, both of the authors have h indices of 2, which is on the low side. They have each published about two dozen papers, had a few hundred references. They both have computer science, mathematics, and engineering as their subject areas which match well with the subject area. Both individuals have affiliations with the University of Lisborn which lends the paper additional credibility.

Precursors and Successors

This work has references in total, many of them have been cited hundreds of times, some over a thousand times. The most cited work was [5] which was cited 4153 times according to Scopus. However, this article is exceedingly old, published in 1979. The reference was book called the Ecological Approach to Visual Perception. Unfortunately, its text could not be found. [6] is cited 1457 times according to Scopus and was key to the development of the visual system and fast response used in the main robotics paper. Since some the concerns of the authors of the robotic paper involved reducing computational stress due to visual perception to increase speed and performance, this article lent it some analysis methods. Another highly cited paper was more biological. [7] was cited 1623 times according to Scopus. This article looked at the computation arrangements in the brain and how that relates to vision. Since the system proposed by the authors of [4] aimed for a bioinspired design with similar computation, this article may have influenced their processing algorithm to be more biologically based. Overall, most of the precursor articles related to different methods of processing visual data. This article only has one successor. This could be influenced by the fact that it was published online on January 4, 2011. Since it is so new, it is unreasonable to expect large amounts of citations. The sole citation was from [8] which drew from models, social insect behavior, and brain behavior to explore cognition.

C.3.8)

Biology Paper

This paper [9] was published in the Annual Review of Entomology which has an SJR of 0.888 and an SNIP of 7.030, implying that the journal is both prestigious and impactful, and so, lends credibility to the article. To further support this point, it has an Impact Factor from the ISI JCR of 11.271 which is the top journal in the field of Entomology. In addition, the author, Kirk P. Visscher has a h index of 15, which is respectable, and is form the University of California, Riverside, Department of Entomology. The author is both in the department that is directly related and from an established university. Both of these facts further lend credibility to the article. It has been cited 35 times according to Scopus.

Precursors and Successors

The most cited work is [1]. This is a book on ants in general written in 1990 which was cited 3792 according to Scopus, the most citations in the list of precursors by far. The second most cited was [11] which was cited 822 times according to Scopus. This again was a book on the behavior of social insects, this time bees and is more recent, being published in the year 2000. The first journal article cited, in terms of number of citations, was [12]. This article moved away from insects and looked at chemical sensing and behavior in bacteria, adding another layer to social behavior in biological organisms. The fact that there are two very general books as two of the citations is inline with the fact that this paper is a review. Also, these are by far not the only citations. The paper cites a total of 103 sources. This paper itself has been cited 35 times with many recent citations. Interestingly, this paper is also cited by [8] which also cites the robotics paper mentioned above. Another paper that cites this biological reference is [13]. This paper relates group decision dynamics in humans to other social animals and explores the decision making process of the different species.

Bibliography
1. E. O. Wilson, & Bert Hölldobler. (1990). The Ants. USA: Belknap Press of Harvard University Press. Retrieved from www.amazon.com/Ants-Bert-Holldobler/dp/0674040759
2. Beekman, M., Fathke, R. L., & Seeley, T. D. (2006). How does an informed minority of scouts guide a honeybee swarm as it flies to its new home?. Animal Behaviour, 71(1), 161-171. Retrieved from www.scopus.com
3. Cao, Y. U., Fukunaga, A. S., & Kahng, A. B. (1997). Cooperative mobile robotics: Antecedents and directions. Autonomous Robots, 4(1), 7-27. Retrieved from www.scopus.com
4. Santana, P., Correia, L. (2011). Swarm cognition on off-road autonomous robots. 5, 45-72.
5. Gibson, J., (1979). The Ecological Approach to Visual Perception.
6. Itti, L., Koch, C., Niebur, E. (1998). A model of saliency-based visual attention for rapid science analysis, 20(11), 1254-1259.
7. Corbetta, M., Shulman, G.L. (2002). Control of goal-directed and stimulus-driven attention in the brain, 3(3), 201-215.
8. Trianni, V., Tuci, E., Passino, K.M., Marshall, J.A.R. (2011). Swarm Cognition: An interdisciplinary approach to the study of self-organising biological collectives. Swarm Intelligence, 5(1), 3018.
9. Visscher, P.K. (2007). Group decision making in nest-site selection among social insects. Annual Review of Entomology, 52, 255-275.
10. Marco Frison, Nam-Luc Tran, Nadir Baiboun, Arne Brutschy, Giovanni Pini, Andrea Roli, Marco Dorigo, and Mauro Birattari. (2010). Self-organized task partitioning in a swarm of robots. In Proceedings of the 7th international conference on Swarm intelligence (ANTS'10), 287-298.
11. Michener, C.D. (2000). Bees of the World. Baltimore, MD: Johns Hopkins Univ. Press. 913.
12. Miller, M.B., Basseler, B.L. (2001) Quorum sensing in bacteria. Annual Review of Microbiology, 55, 165-199.
13. Conradt, L., List, C. (2009). Group decisions in humans and animals: A survey. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1518), 719-742.
14. Frederick Ducatelle, Gianni A. Di Caro, and Luca M. Gambardella. (2010). Cooperative self-organization in a heterogeneous swarm robotic system. In Proceedings of the 12th annual conference on Genetic and evolutionary computation (GECCO '10). ACM, New York, NY, USA, 87-94.
15. Trianni, V., & Dorigo, M. (2006). Self-organisation and communication in groups of simulated and physical robots. Biological Cybernetics, 95(3), 213-231.