Cooperative Trajectory Planning for Multiple UAVs Using Distributed Receding Horizon Control and Inverse Dynamics Optimization Method

Wang, Chao; Gu, Xiaodong; Chen, Jing

doi:10.1007/978-3-319-38789-5_14

Cited by 4 publications

(3 citation statements)

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“…situations is ρK , where ρ is the predefined selection ratio ranging from 0.0 to 1.0. Then, o t ,m , the optimality of the m-th behavior for s t , is calculated using (9).…”

Section: ) Optimal Behavior Inferencementioning

confidence: 99%

“…Advantage matrix -Building the advantage matrix which represents the preference of behaviors -Easy to interpret -High cost for building matrix [4], [5] Genetic algorithm -Exploring the solution space to improve particular objectives -Long time to obtain solutions -Local optimum [6], [7], [8], [9] Reinforcement learning -Finding an optimal policy that maximize total future reward -Global optimum -Difficult to define reward for each behavior [10], [11], [12] Matrix factorization -Estimating latent factors that explain the implcit attributes of state features -Latent factor -Robust to the data sparseness [13] timal behavior by using a predefined situation-behavior (SB) matrix [4], [5], [14]. The results obtained by the methods are easy to interpret because optimal behavior is identified by comparing values of elements in the given AM.…”

Section: Introductionmentioning

confidence: 99%

“…[14]. One of the limitations with this method is that the schemes adjusted for a specific domain are often challenging to apply to similar problems [9].…”

Section: Introductionmentioning

confidence: 99%

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A Behavior Optimization Method for Unmanned Combat Aerial Vehicles Using Matrix Factorization

et al. 2020

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One of the fundamental technologies for unmanned combat aerial vehicles and combat simulators is behavior optimization, which finds a behavior that maximizes the probability of winning a battle. With the advent of military science, combat logs became available, allowing machine learning algorithms to be used for the behavior optimization. Due to implicit attributes such as the experience of an operator that are not explicitly presented in log data, existing methods for behavior optimization have limitations in performance improvement. Furthermore, specific behaviors occur with low frequency, resulting in a dataset with imbalanced and empty values. Therefore, we apply a matrix factorization (MF) method, which is one of latent factor models and known for sophisticated imputation of empty values, to the behavior optimization problem of unmanned combat aerial vehicles. A situation-behavior matrix, whose elements are ratings indicating the optimality of behaviors in situations, is defined to implement the MF based method. Experiments for performance comparison were conducted on combat logs, in which the proposed method yielded satisfactory results. INDEX TERMS behavior optimization, unmanned vehicle, matrix factorization, reinforcement learning, situation-behavior matrix ABBREVIATIONS AM Advantage matrix. FOV Field of view. GA Genetic algorithm. LOS Line of sight. MF Matrix factorization. nDCG Normalized discounted cumulative gain. RL Reinforcement learning. SB Situation-behavior. UV Unmanned vehicle.

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“…situations is ρK , where ρ is the predefined selection ratio ranging from 0.0 to 1.0. Then, o t ,m , the optimality of the m-th behavior for s t , is calculated using (9).…”

Section: ) Optimal Behavior Inferencementioning

confidence: 99%