Abstract
In swarm robotics, hundreds or thousands of robots have to reach a common goal autonomously. Usually, the robots are small and their abilities are very limited. The autonomy of the robots requires that the robots’ behaviors are purely based on their local perceptions, which are usually rather limited. If the robot swarm is able to join multiple instances of individual perceptions to one big global picture (e.g. to collectively construct a sort of map), then the swarm can perform efficiently and such a swarm can target complex tasks. We here present two approaches to realize ‘collective perception’ in a robot swarm. Both require only limited abilities in communication and in calculation. We compare these strategies in different environments and evaluate the swarm’s performance in simulations of fluctuating environmental conditions and with varying parameter settings.
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References
Seyfried, J., Szymanski, M., Bender, N., Estana, R., Thiel, M., Wörn, H.: The I-SWARM Project: Intelligent Small World Autonomous Robots for Micro-Manipulation. In: Şahin, E., Spears, W.M. (eds.) Swarm Robotics. LNCS, vol. 3342, pp. 70–83. Springer, Heidelberg (2005)
Kornienko, S., Kornienko, O., Constantinescu, C., Pradier, M., Levi, P.: Cognitive micro-agents: individual and collective perception in a microrobotic swarm. In: Proceedings of the IJCAI-05 Workshop on Agents in Real-Time and Dynamic Environment, Edinburgh, Scotland, pp. 33–42 (2005)
Kornienko, S., Kornienko, O., Levi, P.: Minimalistic approach towards communication and perception in microrobotic swarms. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, Alberta, Canada, pp. 4005–4011. IEEE, Los Alamitos (2005)
Liu, Y., Passino, K.M.: Biomimicry of Social Foraging Behavior for Distributed Optimization: Models, Principles, and Emergent Behaviors. Journal of Optimization Theory and Applications 115(3), 603–628 (2002)
Camazine, S., Deneubourg, J.L., Franks, N., Sneyd, J., Theraulaz, G., Bonabeau, E.: Self-organization in biological systems. Princeton University Press, Princeton (2001)
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm intelligence: From natural to artificial systems. Oxford University Press, New York (1999)
Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann Publishers, San Francisco (2001)
Anderson, C., Ratnieks, F.L.W.: Task partitioning in insect societies. I. Effect of colony size on queueing delay and colony ergonomic efficiency. Am. Naturalist 154, 521–535 (1999)
Ratnieks, F.L.W., Anderson, C.: Task partitioning in insect societies. II. Use of queueing delay information in recruitment. Am. Naturalist 154, 536–548 (1999)
Seeley, T., Towey, C.: Why search time to find a food-storer bee accurately indicates the relative rates of nectar collecting and nectar processing in honey bee colonies. Animal Behaviour 47, 311–316 (1994)
Pratt, S.C.: Optimal timing of comb construction by honeybee (Apis mellifera) colonies: a dynamic programming model and experimental tests. Behavioral Ecology and Sociobiology 46, 30–42 (1999)
Huang, M., Seeley, T.: Multiple unloadings by nectar foragers in honey bees: a matter of information improvement or crop fullness? Insectes Sociaux 50, 1–10 (2003)
Camazine, S.: The regulation of pollen foraging by honey bees: How foragers assess the colony’s need for pollen. Behavioral Ecology and Sociobiology 32, 265–273 (1993)
Camazine, S., Crailsheim, K., Hrassnigg, N., Robinson, G.E., Leonhard, B., Kropiunigg, H.: Protein trophallaxis and the regulation of pollen foraging by honey bees (Apis mellifera L). Apidologie 29, 113–126 (1998)
Schmickl, T., Crailsheim, K.: Inner nest homeostasis in a changing environment with special emphasis on honeybee brood nursing and pollen supply. Apidologie 35, 249–263 (2004)
Crailsheim, K.: The flow of jelly within a honeybee colony. Journal of Comparative Physiology B 162, 681–689 (1992)
Schmickl, T., Crailsheim, K.: Trophallaxis among swarm-robots: A biological inspired strategy for swarm robotics. In: Proceedings of BioRob 2006, Biomedical Robotics and Biomechatronics, Pisa, Italy (2006)
Valdastri, P., Corradi, P., Menciassi, A., Schmickl, T., Crailsheim, K., Seyfried, J., Dario, P.: Micromanipulation, communication and swarm intelligence issues in a microrobotic platform. In: Robotics and Automation Systems (in press)
Payton, D., Daily, M., Estowski, R., Howard, M., Lee, C.: Pheromone Robotics. Autonomous Robots 11, 319–324 (2001)
Payton, D., Estkowski, R., Howard, M.: Compound behaviors in pheromone robotics. Robotics and Autonomous Systems 44, 229–240 (2003)
Stoy, K.: How do construct dense objects with self-reconfigurable robots. In: Christensen, H.I. (ed.) European Robotics Symposium 2006. STAR, vol. 22, pp. 27–37. Springer, Heidelberg (2006)
McLurkin, J.D.: Stupid robot tricks: a behavior-based distributed algorithm library for programming swarms of robots. Master thesis at the MIT (2004)
Trianni, V., Nolfi, S., Dorigo, M.: Hole Avoidance: Experiments in Coordinated Motion on Rough Terrain. In: Groen, F., Amato, N., Bonarini, A., Yoshida, E., Krose, B. (eds.) Intelligent Autonomous Systems 8, pp. 29–36 (2004)
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Schmickl, T., Möslinger, C., Crailsheim, K. (2007). Collective Perception in a Robot Swarm. In: Şahin, E., Spears, W.M., Winfield, A.F.T. (eds) Swarm Robotics. SR 2006. Lecture Notes in Computer Science, vol 4433. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71541-2_10
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DOI: https://doi.org/10.1007/978-3-540-71541-2_10
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