Construction of Optimal Control Graphs in Multi-robot Systems
Control graphs are used in multi-robot systems to maintain information about which robot senses another robot, and at what position. Control graphs allow robots to localize relative to others, and maintain stable formations. Previous work makes two critical assumptions. First, it assumes edge weights of control graphs are deterministic scalars, while in reality they represent complex stochastic factors. Second, it assumes that a single robot is pre-determined to serve as the global anchor for the robots’ relative estimates. However, optimal selection of this robot is an open problem. In this work, we address these two issues. We show that existing work may be recast as graph-theoretic algorithms inducing control graphs for more general representation of the sensing capabilities of robots. We then formulate the problem of optimal selection of an anchor, and present a centralized algorithm for solving it. We evaluate use of these algorithm on physical and simulated robots and show they very significantly improve on existing work.
We gratefully acknowledge support by ISF grants #1511/12 and #1337/15. As always, thanks to K. Ushi.
- 1.Cormen, T.T., Leiserson, C.E., Rivest, R.L.: Introduction to Algorithms. MIT Press, USA (1990)Google Scholar
- 2.Desai, J.P.: Modeling multiple teams of mobile robots: a graph-theoretic approach. IROS 1, 381–386 (2001)Google Scholar
- 4.Howard, A., Matarić, M.J., Sukhatme, G.S.: Putting the ‘i’ in ‘team’: an ego-centric approach to cooperative localization. In: ICRA, pp. 868–892 (2003)Google Scholar
- 5.Hwang, L.K.: Stochastic shortest path algorithm based on lagrangian relaxation. Master’s thesis, University of Illinois at Urbana-Champaign (2010)Google Scholar
- 7.Lemay, M., Michaud, F., Létourneau, D., Valin, J.M.: Autonomous initialization of robot formations. In: ICRA-04 (2004)Google Scholar
- 8.Loui, R.P.: Optimal paths in graphs with stochastic or multidimensional weights. Technical report TR115, Computer Science Department, University of Rochester (1982)Google Scholar
- 9.Martinelli, A., Pont, F., Siegwart, R.: Multi-robot localization using relative observations. In: ICRA-05, pp. 2797–2802. IEEE (2005)Google Scholar
- 10.Nagavalli, S., Lybarger, A., Luo, L., Chakraborty, N., Sycara, K.: Aligning coordinate frames in multi-robot systems with relative sensing information. In: IROS-14, pp. 388–395. IEEE (2014)Google Scholar
- 12.Traub, M., Kaminka, G.A., Agmon, N.: Who goes there? using social regret to select a robot to reach a goal. In: AAMAS-11 (2011)Google Scholar
- 13.Trawny, N., Zhou, X.S., Zhou, K., Roumeliotis, S.I.: Interrobot transformations in 3-d. IEEE Trans. Robot. 26(2), 226–243 (2010). https://doi.org/10.1109/TRO.2010.2042539