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Journal of Intelligent & Robotic Systems

, Volume 95, Issue 1, pp 119–135 | Cite as

Multiagent Pursuit-Evasion Problem with the Pursuers Moving at Uncertain Speeds

  • Fuhan Yan
  • Jiuchuan Jiang
  • Kai Di
  • Yichuan JiangEmail author
  • Zhifeng Hao
Article

Abstract

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.

Keywords

Multiagent pursuit-evasion problem Uncertain speeds Pursuing strategy 

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Notes

Acknowledgements

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).

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  • Fuhan Yan
    • 1
    • 2
  • Jiuchuan Jiang
    • 3
  • Kai Di
    • 1
    • 2
  • Yichuan Jiang
    • 1
    • 2
    Email author
  • Zhifeng Hao
    • 1
  1. 1.School of Mathematics and Big DataFoshan UniversityFoshanChina
  2. 2.School of Computer Science and EngineeringSoutheast UniversityNanjingChina
  3. 3.School of Computer Science and EngineeringNanyang Technological UniversitySingaporeSingapore

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