An integer linear programming model for fair multitarget tracking in cooperative multirobot systems
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Cooperative Multi-Robot Observation of Multiple Moving Targets (CMOMMT) denotes a class of problems in which a set of autonomous mobile robots equipped with limited-range sensors keep under observation a (possibly larger) set of mobile targets. In the existing literature, it is common to let the robots cooperatively plan their motion in order to maximize the average targets’ detection rate, defined as the percentage of mission steps in which a target is observed by at least one robot. We present a novel optimization model for CMOMMT scenarios which features fairness of observation among different targets as an additional objective. The proposed integer linear formulation exploits available knowledge about the expected motion patterns of the targets, represented as a probabilistic occupancy maps estimated in a Bayesian framework. An empirical analysis of the model is performed in simulation, considering multiple scenarios to study the effects of the amount of robots and of the prediction accuracy for the mobility of the targets. Both centralized and distributed implementations are presented and compared to each other evaluating the impact of multi-hop communications and limited information sharing. The proposed solutions are also compared to two algorithms selected from the literature. The model is finally validated on a real team of ground robots in a limited set of scenarios.
KeywordsMultirobot systems Cooperative target tracking Fair resource allocation
The authors would like to thank Nicola Basilico for useful discussions about this work.
Supplementary material 1 (mp4 17879 KB)
- Banfi, J., Guzzi, J., Giusti, A., Gambardella, L., & Di Caro, G. (2015). Fair multi-target tracking in cooperative multi-robot systems. In Proceedings of ICRA (pp. 5411–5418).Google Scholar
- Bertuccelli, L. F., & How, J. P. (2005). Robust UAV search for environments with imprecise probability maps. In Proceedings of CDC (pp. 5680–5685).Google Scholar
- Bertuccelli, L. F., & How, J. P. (2006). Search for dynamic targets with uncertain probability maps. In Proceedings of ACC (pp. 737–742).Google Scholar
- Ding, Y., Zhu, M., He, Y., & Jiang, J. (2006). P-CMOMMT algorithm for the cooperative multi-robot observation of multiple moving targets. In Proceedings of WCICA (pp. 9267–9271).Google Scholar
- Elmogy, A. M., Khamis, A. M., & Karray, F. O. (2012). Market-based approach to mobile surveillance systems. Journal of Robotics, 2012, 841291.Google Scholar
- Gurobi Optimization. (2014). Gurobi optimizer reference manual. http://www.gurobi.com. Accessed 12 Apr 2018.
- Guzzi, J., Giusti, A., Gambardella, L., & Di Caro, G. A. (2013). Human-friendly robot navigation in dynamic environments. In Proceedings of ICRA (pp 423–430).Google Scholar
- Jung, B., & Sukhatme, G. S. (2006). Cooperative multi-robot target tracking. In Proceedings of DARS (pp. 81–90).Google Scholar
- Khan, A., Rinner, B., & Cavallaro, A. (2016). Cooperative robots to observe moving targets: Review. IEEE Transactions on Cybernetics, PP(99), 1–12. (Available online).Google Scholar
- Kolling, A., & Carpin, S. (2006). Multirobot cooperation for surveillance of multiple moving targets-a new behavioral approach. In Proceedings of ICRA (pp. 1311–1316).Google Scholar
- Luke, S., Sullivan, K., Panait, L., & Balan, G. (2005). Tunably decentralized algorithms for cooperative target observation. In Proceedings of AAMAS (pp. 911–917).Google Scholar
- Mercier, L., & Van Hentenryck, P. (2007). Performance analysis of online anticipatory algorithms for large multistage stochastic integer programs. In Proceedings of IJCAI (pp. 1979–1984).Google Scholar
- Parker, L. E., & Emmons, B. A. (1997). Cooperative multi-robot observation of multiple moving targets. Proceeding of ICRA (Vol. 3, pp. 2082–2089).Google Scholar
- Silver, D. (2005). Cooperative pathfinding. In Proceedings of AIIDE (pp. 117–122).Google Scholar
- Standley, T., & Korf, R. (2011). Complete algorithms for cooperative pathfinding problems. In Proceedings of IJCAI (pp. 668–673).Google Scholar