Design of ant-inspired stochastic control policies for collective transport by robotic swarms
In this paper, we present an approach to designing decentralized robot control policies that mimic certain microscopic and macroscopic behaviors of ants performing collective transport tasks. In prior work, we used a stochastic hybrid system model to characterize the observed team dynamics of ant group retrieval of a rigid load. We have also used macroscopic population dynamic models to design enzyme-inspired stochastic control policies that allocate a robotic swarm around multiple boundaries in a way that is robust to environmental variations. Here, we build on this prior work to synthesize stochastic robot attachment–detachment policies for tasks in which a robotic swarm must achieve non-uniform spatial distributions around multiple loads and transport them at a constant velocity. Three methods are presented for designing robot control policies that replicate the steady-state distributions, transient dynamics, and fluxes between states that we have observed in ant populations during group retrieval. The equilibrium population matching method can be used to achieve a desired transport team composition as quickly as possible; the transient matching method can control the transient population dynamics of the team while driving it to the desired composition; and the rate matching method regulates the rates at which robots join and leave a load during transport. We validate our model predictions in an agent-based simulation, verify that each controller design method produces successful transport of a load at a regulated velocity, and compare the advantages and disadvantages of each method.
KeywordsCollective transport Bio-inspired robotics Stochastic robotics Stochastic hybrid system Distributed robotic system
Comments by Simon DeDeo regarding the philosophical implications of equilibria of irreversible processes have been helpful in framing our thoughts about this approach. Interaction with him was possible due to a workshop on Information, Complexity, and Life organized by the BEYOND Center for Fundamental Concepts in Science at Arizona State University. We are also grateful for the helpful comments of four anonymous referees. This work was supported in part by NSF Award no. CCF-1012029, NSF Award No. CMMI-1363499, and DARPA Young Faculty Award No. D14AP00054.
- Becker, A., Habibi, G., Werfel, J., Rubenstein, M., & McLurkin, J. (2013). Massive uniform manipulation: Controlling large populations of simple robots with a common input signal. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Tokyo, Japan.Google Scholar
- Chen, J., Gauci, M., & Groß, R. (2013a) A strategy for transporting tall objects with a swarm of miniature mobile robots. Proceedings of the 2013 International Conference on Robotics and Automation, Karlsruhe, Germany, pp 863–869, doi: 10.1109/ICRA.2013.6630674.
- Correll, N. (2008). Parameter estimation and optimal control of swarm-robotic systems: A case study in distributed task allocation. Proceedings of the 2008 IEEE International Conference on Robotics and Automation, IEEE, Pasadena, CA, USA, pp 3302–3307, doi: 10.1109/ROBOT.2008.4543714.
- Correll, N., & Martinoli, A. (2004). Modeling and optimization of a swarm-intelligent inspection system. Proceedings of the Seventh International Symposium on Distributed Autonomous Robotics Systems (DARS 2004), Toulouse, France, pp 369–378, doi: 10.1007/978-4-431-35873-2_36.
- Czaczkes, T. J., & Ratnieks, F. L. W. (2013). Cooperative transport in ants (Hymenoptera: Formicidae) and elsewhere. Myrmecological News, 18, 1–11.Google Scholar
- Ferrante, E., Brambilla, M., Birattari, M., & Dorigo, M. (2013). Socially-mediated negotiation for obstacle avoidance in collective transport. Proceedings of International Symposium on Distributed Autonomous Robotic Systems (DARS), Springer, Naples, Italy, Springer Tracts in Advanced Robotics, vol 83, pp 571–583.Google Scholar
- Finch SR (2003) Mathematical constants, encyclopedia of mathematics and its applications, Vol. 94. Cambridge: Cambridge University Press.Google Scholar
- Grassé, P. P. (1959). La reconstruction du nid et les coordinations interindividuelles chez Bellicositermes natalensis et Cubitermes sp. la théorie de la stigmergie: essai d’interprétation du comportement des termites constructeurs. Insectes Sociaux, 6(1), 41–80. doi: 10.1007/BF02223791.CrossRefMathSciNetGoogle Scholar
- Gurarie, E. (2008). Models and analysis of animal movements: From individual tracks to mass dispersal. PhD thesis, University of Washington.Google Scholar
- Kumar, G. P., Buffin, A., Pavlic, T. P., Pratt, S. C., & Berman, S. M. (2013). A stochastic hybrid system model of collective transport in the desert ant Aphaenogaster cockerelli. Proceedings of the 16th ACM International Conference on Hybrid Systems: Computation and Control, Philadelphia, PA, pp. 119–124, doi: 10.1145/2461328.2461349.
- Lachmann, M., & Sella, G. (1995). The computationally complete ant colony: global coordination in a system with no hierarchy. Proceedings of the Third European Conference on Artificial Life (pp. 784–800). Spain: Granada.Google Scholar
- Mather, T. W., & Hsieh, M. A. (2011). Distributed robot ensemble control for deployment to multiple sites. Proceedings of Robotics: Science and Systems VII, Los Angeles, CA, USA.Google Scholar
- Matthey, L., Berman, S., & Kumar, V. (2009). Stochastic strategies for a swarm robotic assembly system. Proceedings of the 2009 IEEE International Conference on Robotics and Automation (pp. 1953–1958). Japan: Kobe.Google Scholar
- Napp, N., Burden, S., & Klavins, E. (2009). Setpoint regulation for stochastically interacting robots. Proceedings of Robotics: Science and Systems V, Seattle, WA, USA.Google Scholar
- O’Grady, R., Pinciroli, C., Groß, R., Christensen, A. L., Mondada, F., Bonani, M., & Dorigo, M. (2009). Swarm-bots to the rescue. In: Proceedings of the 10th European Conference on Artificial Life, Springer, Budapest, Hungary, Lecture Notes in Computer Science, Vol. 5777, pp 165–172, doi: 10.1007/978-3-642-21283-3_21.
- Pavlic, T. P., Wilson, S., Kumar, G. P., & Berman, S. (2013). An enzyme-inspired approach to stochastic allocation of robotic swarms around boundaries. Proceedings of the 16th International Symposium on Robotics Research (ISRR 2013), Singapore.Google Scholar
- Pavlic, T. P., Wilson, S., Kumar, G. P., Berman, S. (2014). Control of stochastic boundary coverage by multi-robot systems. ASME Journal of Dynamic Systems, Measurement and Control.Google Scholar
- Rubenstein, M., Cabrera, A., Werfel, J., Habibi, G., McLurkin, J., & Nagpal, R. (2013). Collective transport of complex objects by simple robots: theory and experiments. Proceedings of the 2013 International Conference on Autonomous Agents and Multi-Agent Systems, Saint Paul, Minnesota, USA, http://dl.acm.org/citation.cfm?id=2484920.2484932. Accessed 16 Feb 2014.
- Stilwell, D., & Bay, J. (1993). Toward the development of a material transport system using swarms of ant-like robots. Proceedings of the International Conference on Robotics and Automation (ICRA), pp 766–771, doi: 10.1109/ROBOT.1993.292070.
- Sugawara, K., Reishus, D., & Correll, N. (2012) Object transportation by granular convection using swarm robots. Distributed autonomous robotic systems, Springer, Baltimore, MD, STAR, http://spot.colorado.edu/dure7259/papers/Sugawara12.pdf. Accessed 7 Jan 2014.
- Talbot, J., Tarjus, G., Van Tassel, P. R., & Viot, P. (2000). From car parking to protein adsorption: An overview of sequential adsorption processes. Colloids Surfaces A: Physicochemical and Engineering Aspects, 165(1–3), 287–324. doi: 10.1016/S0927-7757(99)00409-4.
- Wang, S., & Dormidontova, E. E. (2012). Selectivity of ligand–receptor interactions between nanoparticle and cell surfaces. Physical Review Letters, 109(238), 102.Google Scholar
- Wilensky, U. (1999). NetLogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL, USA, http://ccl.northwestern.edu/netlogo/. Accessed 29 July 2013.