Multiagent Pursuit-Evasion Problem with the Pursuers Moving at Uncertain Speeds
- 216 Downloads
The multiagent pursuit-evasion problems have been widely investigated in related areas. Previous studies usually assumed that the pursuers move at certain speeds. However, in many circumstances the above assumption does not match the peculiarities of real pursuit-evasion cases in which the pursuers’ speeds may be uncertain. Therefore, this paper investigates the multiagent pursuit-evasion problem under the situation in which the pursuers move at uncertain speeds. The new problems of multiagent pursuit-evasion caused by the uncertainty of the pursuers’ speeds include: 1) many previous strategies plan pursuers’ paths based on their speeds, but the uncertainty of speeds will make the pursuers move to worthless target points; 2) previous strategies usually let each pursuer move to a scheduled location, but the uncertainty of speeds may make some pursuers fail to reach the scheduled locations punctually. Aiming at addressing these problems, we present the strategy which lets each pursuer flexibly help the slow neighboring pursuer. As the pursuers’ speeds are uncertain, the optimal decision of pursuers cannot be calculated directly. Thus, we analyze the alternative decision space of pursuers, which contains the decisions that may be optimal and does not contain the obviously bad decisions (such as moving away from the evader). Then, we compare the decisions in the alternative decision space based on simulated annealing resulting that the optimal decision may be selected after repeatedly comparing different decisions. The experimental results show that our strategy can generally outperform previous strategies when the pursuers’ speeds are uncertain.
KeywordsMultiagent pursuit-evasion problem Uncertain speeds Pursuing strategy
Unable to display preview. Download preview PDF.
This work was supported by the National Natural Science Foundation of China (61472079, 61170164, and 61472089), the Natural Science Foundation of Jiangsu Province of China (BK20171363), the Joint Fund of the National Natural Science Foundation of China and Guangdong Province (U1501254), the Science and Technology Planning Project of Guangdong Province (2015B010131015 and 2015B010108006), and the Natural Science Foundation of Guangdong Province (2014A030308008).
- 1.Alexopoulos, A., Schmidt, T., Badreddin, E.: Cooperative pursue in pursuit-evasion games with unmanned aerial vehicles. In: Proceedings of the 27th IEEE/RSJ international conference on intelligent robots and systems (IROS), pp. 4538–4543. IEEE (2015)Google Scholar
- 3.Barrett, S., Stone, P., Kraus, S.: Empirical evaluation of ad hoc teamwork in the pursuit domain. In: Proceedings of the 10th international conference on autonomous agents and multiagent systems-volume 2 (AAMAS), pp. 567–574. International Foundation for Autonomous Agents and Multiagent Systems (2011)Google Scholar
- 4.Bhattacharya, S., Başar, T., Falcone, M.: Numerical approximation for a visibility based pursuit-evasion game. In: Proceedings of the 26th IEEE/RSJ international conference on intelligent robots and systems (IROS), pp. 68–75. IEEE (2014)Google Scholar
- 8.Coxeter, H.S.M., Greitzer, S.L.: Geometry revisited, vol. 19 Maa (1967)Google Scholar
- 10.Hamill, L., Gilbert, N.: Social circles: a simple structure for agent-based social network models. Journal of Artificial Societies and Social Simulation 12(2), 3 (2009)Google Scholar
- 11.Huang, X., Maupin, P., Van Der Meyden, R.: Model checking knowledge in pursuit evasion games. In: Proceedings of the 22nd international joint conference on artificial intelligence (IJCAI), pp. 240–245 (2011)Google Scholar
- 12.Huang, X., Van Der Meyden, R.: Synthesizing strategies for epistemic goals by epistemic model checking: an application to pursuit evasion games. In: Proceedings of the 26th AAAI conference on artificial intelligence (AAAI), pp. 772–778 (2012)Google Scholar
- 13.Jin, S., Qu, Z.: Pursuit-evasion games with multi-pursuer vs. one fast evader. In: Proceedings of the 8th World congress on intelligent control and automation (WCICA), pp. 3184–3189. IEEE (2010)Google Scholar
- 14.Kim, K.Y., Kwon, O., Yeon, J.S., Park, J.H.: Elliptic trajectory generation for galloping quadruped robots. In: Proceedings of the 3rd IEEE international conference on robotics and biomimetics (ROBIO), pp. 103–108. IEEE (2006)Google Scholar
- 16.Kuo, J.Y., Yu, H.F., Liu, K.F.R., Lee, F.W.: Multiagent cooperative learning strategies for pursuit-evasion games. Math. Probl. Eng. 2015 (2015)Google Scholar
- 17.Liemhetcharat, S., Yan, R., Tee, K.P.: Continuous foraging and information gathering in a multi-agent team. In: Proceedings of the 14th international conference on autonomous agents and multiagent systems, pp. 1325–1333. International Foundation for Autonomous Agents and Multiagent Systems (2015)Google Scholar
- 18.Papadopoulos, D., Buehler, M.: Stable running in a quadruped robot with compliant legs. In: Proceedings of the IEEE international conference on robotics and automation, vol. 1, pp. 444–449. IEEE (2000)Google Scholar
- 19.Raboin, E., Kuter, U., Nau, D.: Generating strategies for multi-agent pursuit-evasion games in partially observable euclidean space. In: Proceedings of the 11th international conference on autonomous agents and multiagent systems-volume 3 (AAMAS), pp. 1201–1202. International Foundation for Autonomous Agents and Multiagent Systems (2012)Google Scholar
- 25.Wang, H., Yue, Q., Liu, J.: Research on pursuit-evasion games with multiple heterogeneous pursuers and a high speed evader. In: Proceedings of the 27th Chinese control and decision conference (CCDC), pp. 4366–4370. IEEE (2015)Google Scholar
- 26.Williams, R.: Tokyo police are using drones with nets to catch other drones (2015)Google Scholar
- 27.Yan, F., Jiang, Y.: Pursuing a faster evader based on an agent team with unstable speeds. In: Proceedings of the 16th conference on autonomous agents and multiagent systems, pp. 1766–1768. International Foundation for Autonomous Agents and Multiagent Systems (2017)Google Scholar
- 28.Yasuda, T., Ohkura, K., Nomura, T., Matsumura, Y.: Evolutionary swarm robotics approach to a pursuit problem. In: Proceedings of the 4th IEEE symposium on robotic intelligence in informationally structured space (RiiSS), pp. 1–6. IEEE (2014)Google Scholar