An Alternative Pursuit Strategy in the Game of Obstacle Tag

In this study, we used Cricket equipments based on wireless sensor networks to study the pursuit-evasion strategy of multi-robots in a continuous map. In order to improve the efficiency, two new pursuit-evasion strategies were proposed. To meet the need of a continuous map, a greedy algorithm was utilized to deal with boundary problem when a pursuer or an evader was near the field boundary. The statistical results show the converging-attack pursuit strategy outperforms in pursuing the evader with different escaping strategies, such as randomly moving, active escaping strategy and vector-wards escaping strategy. The vector-wards escaping strategy is also effective in delaying the capture of evaders.

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Geodesic Parallel Pursuit Strategy in a Simple Motion Pursuit Game on the Sphere

Advances in Dynamic Games and Applications ◽

10.1007/978-1-4612-1336-9_5 ◽

2000 ◽

pp. 97-113

Author(s):

A. M. Kovshov

Keyword(s):

Simple Motion ◽

Pursuit Game ◽

Pursuit Strategy

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Two-Stage Pursuit Strategy for Incomplete-Information Impulsive Space Pursuit-Evasion Mission Using Reinforcement Learning

Aerospace ◽

10.3390/aerospace8100299 ◽

2021 ◽

Vol 8 (10) ◽

pp. 299

Author(s):

Bin Yang ◽

Pengxuan Liu ◽

Jinglang Feng ◽

Shuang Li

Keyword(s):

Reinforcement Learning ◽

Incomplete Information ◽

Trajectory Optimization ◽

Game Problem ◽

Gradient Algorithm ◽

Two Stage ◽

Pursuit Evasion ◽

Evasion Game ◽

Close Distance ◽

Pursuit Strategy

This paper presents a novel and robust two-stage pursuit strategy for the incomplete-information impulsive space pursuit-evasion missions considering the J2 perturbation. The strategy firstly models the impulsive pursuit-evasion game problem into a far-distance rendezvous stage and a close-distance game stage according to the perception range of the evader. For the far-distance rendezvous stage, it is transformed into a rendezvous trajectory optimization problem and a new objective function is proposed to obtain the pursuit trajectory with the optimal terminal pursuit capability. For the close-distance game stage, a closed-loop pursuit approach is proposed using one of the reinforcement learning algorithms, i.e., the deep deterministic policy gradient algorithm, to solve and update the pursuit trajectory for the incomplete-information impulsive pursuit-evasion missions. The feasibility of this novel strategy and its robustness to different initial states of the pursuer and evader and to the evasion strategies are demonstrated for the sun-synchronous orbit pursuit-evasion game scenarios. The results of the Monte Carlo tests show that the successful pursuit ratio of the proposed method is over 91% for all the given scenarios.

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