Abstract
Recent work in socio-biological sciences have introduced simple heuristics that accurately explain the behavior of pedestrians navigating in an environment while avoiding mutual collisions. We have adapted and implemented such heuristics for distributed obstacle avoidance in robot swarms, with the goal of obtaining human-like navigation behaviors which would be perceived as friendly by humans sharing the same spaces. In this context, we study the effects of using different sensing modalities and robot types, and introduce robot’s emotional states, which allows us to modulate system’s group behavior. Experimental results are provided for both real and simulated robots. The extensive quantitative simulations show the macroscopic behavior of the system in various scenarios, where we observe emergent collective behaviors – some of which are similar to those observed in human crowds.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Moussaïd, M., Helbing, D., Theraulaz, G.: How simple rules determine pedestrian behavior and crowd disasters. Proc. Natl. Acad. Sci. USA 108(17), 6884–6888 (2011)
Bonani, M., Longchamp, V., Magnenat, S., Rétornaz, P., Burnier, D., Roulet, G., Vaussard, F., Bleuler, H., Mondada, F.: The marXbot, a miniature mobile robot opening new perspectives for the collective-robotic research. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4187–4193 (2010)
Shillert, Z., Fiorini, P.: Motion planning in dynamic environments using velocity obstacles. Int. J. Robot. Res. 17(7), 760–772 (1998)
Kluge, B., Prassler, E.: Recursive agent modeling with probabilistic velocity obstacles for mobile robot navigation among humans. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 1, pp. 376–381 (2003)
van den Berg, J., Manocha, D., Lin, M.: Reciprocal velocity obstacles for real-time multi-agent navigation. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 1928–1935 (2008)
Snape, J., van den Berg, J., Guy, S.J., Manocha, D.: The hybrid reciprocal velocity obstacle. IEEE Trans. Robot. 27(4), 696–706 (2011)
Guy, S.J., Lin, M.C., Manocha, D.: Modeling collision avoidance behavior for virtual humans. In: Proceedings of the International Conference on Autonomous Agents and Multiagent Systems, pp. 575–582 (2010)
Gibson, J.J.: Visually controlled locomotion and visual orientation in animals. Br. J. Psychol. 49(3), 182–194 (1958)
François, M., Morice, A.H., Bootsma, R.J., Montagne, G.: Visual control of walking velocity. Neurosci. Res. 70(2), 214–219 (2011)
Bootsma, R.J., Craig, C.M.: Information used in detecting upcoming collision. Perception 32(5), 525–544 (2003)
Lee, D.N.: A theory of visual control of braking based on information about time-to-collision. Perception 5(4), 437–459 (1976)
Coull, J.T., Vidal, F., Goulon, C., Nazarian, B., Craig, C.: Using time-to-contact information to assess potential collision modulates both visual and temporal prediction networks. Front. Hum. Neurosci. 2, 10 (2008)
Krichmar, J.L.: The neuromodulatory system: a framework for survival and adaptive behavior in a challenging world. Adapt. Behav. 16(6), 385–399 (2008)
Cox, B.R., Krichmar, J.L.: Neuromodulation as a robot controller: a brain inspired strategy for controlling autonomous robots. IEEE Robot. Autom. Mag. 16(3), 1–25 (2009)
Krichmar, J.L.: A biologically inspired action selection algorithm based on principles of neuromodulation. In: Proceedings of IEEE World Congress on Computational Intelligence, pp. 10–15 (2012)
Hall, E.T.: A system for the notation of proxemic behavior. Am. Anthropol. 65(5), 1003–1026 (1963)
Helbing, D., Farkas, I., Vicsek, T.: Simulating dynamical features of escape panic. Nature 407(6803), 487–490 (2000)
Johansson, A.: Constant-net-time headway as key mechanism behind pedestrian flow dynamics. Phys. Rev. E 80, 026120 (2009)
Moussaïd, M., Guillot, E.G., Moreau, M., Fehrenbach, J., Chabiron, O., Lemercier, S., Pettré, J., Appert-Rolland, C., Degond, P., Theraulaz, G.: Traffic instabilities in self-organized pedestrian crowds. PLoS Comput. Biol. 8(3), e1002442 (2012)
Helbing, D., Molnár, P., Farkas, I.J., Bolay, K.: Self-organizing pedestrian movement. Environ. Plan. B Plan. Des. 28(3), 361–383 (2001)
Moussaïd, M., Helbing, D., Garnier, S., Johansson, A., Combe, M., Theraulaz, G.: Experimental study of the behavioural mechanisms underlying self-organization in human crowds. Proc. R. Soc. B 276(1668), 2755–2762 (2009)
Dorigo, M., Floreano, D., Gambardella, L.M., Mondada, F., Nolfi, S., Baaboura, T., Birattari, M., Bonani, M., Brambilla, M., Brutschy, A., Burnier, D., Campo, A., Christensen, A.L., Decugnière, A., Di Caro, G.A., Ducatelle, F., Ferrante, E., Förster, A., Martinez Gonzales, J., Guzzi, J., Longchamp, V., Magnenat, S., Mathews, N., Montes de Oca, M., O’Grady, R., Pinciroli, C., Pini, G., Rétornaz, P., Roberts, J., Sperati, V., Stirling, T., Stranieri, A., Stützle, T., Trianni, V., Tuci, E., Turgut, A.E., Vaussard, F.: Swarmanoid: a novel concept for the study of heterogeneous robotic swarms. IEEE Robot. Autom. Mag. (2012, to appear)
Yamori, K.: Going with the flow: micro–macro dynamics in the macrobehavioral patterns of pedestrian crowds. Psychol. Rev. 105(3), 530–557 (1998)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Guzzi, J., Giusti, A., Gambardella, L.M., Di Caro, G.A. (2014). Bioinspired Obstacle Avoidance Algorithms for Robot Swarms. In: Di Caro, G., Theraulaz, G. (eds) Bio-Inspired Models of Network, Information, and Computing Systems. BIONETICS 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 134. Springer, Cham. https://doi.org/10.1007/978-3-319-06944-9_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-06944-9_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06943-2
Online ISBN: 978-3-319-06944-9
eBook Packages: Computer ScienceComputer Science (R0)