scholarly journals Minimization of energy costs for UAV management in a conflict task

2021 ◽  
Vol 279 ◽  
pp. 01006
Author(s):  
Viktor Lapshin ◽  
Stanislav Ivanov
Keyword(s):  
Control Strategy ◽  
Game Problem ◽  
Energy Losses ◽  
Energy Costs ◽  
State Variables ◽  
Conflict Task ◽  
Pursuit Evasion ◽  
Evasion Game ◽  
Differential Pursuit ◽  
Linear Differential

This article considers the method of developing an evader control strategy in the non-linear differential pursuit-evasion game problem. It is assumed that the pursuer resorts to the most probable control strategy in order to capture the evader and that at each moment the evader knows its own and the enemy’s physical capabilities. This assumption allows to bring the game problem down to the problem of a unilateral evader control, with the condition of reaching a saddle point not obligatory to be fulfilled. The control is realised in the form of synthesis and additionally ensures that the requirements for bringing the evader to a specified area with terminal optimization of certain state variables are satisfiedt. The solution of this problem will significantly reduce the energy losses for controlling an unmanned vehicle, the possible effect is to save 15-20 % of fuel with a probability of 0.98, to solve the problem of chasing the enemy.

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

Aerospace ◽  
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.

scholarly journals Qualitative criterion for interception in a pursuit/evasion game

2009 ◽  
Vol 466 (2117) ◽  
pp. 1365-1371 ◽  
Author(s):  
J. A. Morgan
Keyword(s):  
Nash Equilibrium ◽  
Initial Position ◽  
Initial Time ◽  
Sufficient Condition ◽  
Pursuit Evasion ◽  
Evasion Game ◽  
Differential Pursuit ◽  
The Future ◽  
Qualitative Criterion ◽  
Future Cone

A qualitative account is given of a differential pursuit/evasion game. A criterion for the existence of an intercept solution is obtained using future cones that contain all attainable trajectories of target or interceptor originating from an initial position. A sufficient and necessary conditon that an opportunity to intercept always exists is that, after some initial time, the future cone of the target be contained within the future cone of the interceptor. The sufficient condition may be regarded as a kind of Nash equilibrium.

scholarly journals An Improved Approach towards Multi-Agent Pursuit–Evasion Game Decision-Making Using Deep Reinforcement Learning

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1433
Author(s):  
Kaifang Wan ◽  
Dingwei Wu ◽  
Yiwei Zhai ◽  
Bo Li ◽  
Xiaoguang Gao ◽  
...  
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Superior Performance ◽  
State Variables ◽  
Multi Agent Systems ◽  
Adversarial Learning ◽  
Pursuit Evasion ◽  
Evasion Game ◽  
Multi Agent ◽  
Adversarial Attack

A pursuit–evasion game is a classical maneuver confrontation problem in the multi-agent systems (MASs) domain. An online decision technique based on deep reinforcement learning (DRL) was developed in this paper to address the problem of environment sensing and decision-making in pursuit–evasion games. A control-oriented framework developed from the DRL-based multi-agent deep deterministic policy gradient (MADDPG) algorithm was built to implement multi-agent cooperative decision-making to overcome the limitation of the tedious state variables required for the traditionally complicated modeling process. To address the effects of errors between a model and a real scenario, this paper introduces adversarial disturbances. It also proposes a novel adversarial attack trick and adversarial learning MADDPG (A2-MADDPG) algorithm. By introducing an adversarial attack trick for the agents themselves, uncertainties of the real world are modeled, thereby optimizing robust training. During the training process, adversarial learning was incorporated into our algorithm to preprocess the actions of multiple agents, which enabled them to properly respond to uncertain dynamic changes in MASs. Experimental results verified that the proposed approach provides superior performance and effectiveness for pursuers and evaders, and both can learn the corresponding confrontational strategy during training.

Modeling and Solving of the Missile Pursuit-Evasion Game Problem

2021 ◽  
Author(s):  
Dingding Qi ◽  
Longyue Li ◽  
Hailong Xu ◽  
Ye Tian ◽  
Huizhen Zhao
Keyword(s):  
Game Problem ◽  
Pursuit Evasion ◽  
Evasion Game

scholarly journals Pursuer’s Control Strategy for Orbital Pursuit-Evasion-Defense Game with Continuous Low Thrust Propulsion

2019 ◽  
Vol 9 (15) ◽  
pp. 3190
Author(s):  
Junfeng Zhou ◽  
Lin Zhao ◽  
Jianhua Cheng ◽  
Shuo Wang ◽  
Yipeng Wang
Keyword(s):  
Control Strategy ◽  
Comprehensive Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Multiple Shooting ◽  
State Variables ◽  
Low Thrust ◽  
Optimal Control Strategy ◽  
Pursuit Evasion ◽  
Multi Objective Genetic Algorithm ◽  
Multiple Shooting Method

This paper studies the orbital pursuit-evasion-defense problem with the continuous low thrust propulsion. A control strategy for the pursuer is proposed based on the fuzzy comprehensive evaluation and the differential game. First, the system is described by the Lawden’s equations, and simplified by introducing the relative state variables and the zero effort miss (ZEM) variables. Then, the objective function of the pursuer is designed based on the fuzzy comprehensive evaluation, and the analytical necessary conditions for the optimal control strategy are presented. Finally, a hybrid method combining the multi-objective genetic algorithm and the multiple shooting method is proposed to obtain the solution of the orbital pursuit-evasion-defense problem. The simulation results show that the proposed control strategy can handle the orbital pursuit-evasion-defense problem effectively.

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