An Estimation Method for Arrival Time at the Farthest Point in Case of Optimal Deployment of Heterogeneous Multi-agent Problems

2021 ◽  
Vol 57 (5) ◽  
pp. 245-252
Author(s):  
Kisho ICHIHARA ◽  
Seiya UENO
Keyword(s):  
Arrival Time ◽  
Estimation Method ◽  
Multi Agent ◽  
Optimal Deployment ◽  
Farthest Point

Nonuniform Deployment of Autonomous Agents in Harbor-Like Environments

2014 ◽  
Vol 02 (04) ◽  
pp. 377-389 ◽  
Author(s):  
Suruz Miah ◽  
Bao Nguyen ◽  
François-Alex Bourque ◽  
Davide Spinello
Keyword(s):  
Autonomous Agents ◽  
Optimal Trajectories ◽  
Risk Map ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Deployment Strategy ◽  
Area Of Interest ◽  
Multi Agent ◽  
Optimal Deployment ◽  
Theoretical Results

We propose a nonuniform deployment strategy of a group of homogeneous autonomous agents in harbor-like environments. High value units berthed in the area need to be secured against external attacks. Defenders deployed in the area are expected to monitor, intercept, engage, and neutralize threats. In the framework of decentralized coordinated multi-agent systems, we model and simulate the optimal deployment of a group of mobile autonomous agents that accounts for a risk map of the area and the optimal trajectories that minimize the energy consumed to intercept a threat in a given area of interest. Theoretical results are numerically illustrated through simulations in a realistic harbor protection scenario.

Four-Dimensional Trajectory Generation for UAVs Based on Multi-Agent Q Learning

2020 ◽  
Vol 73 (4) ◽  
pp. 874-891
Author(s):  
Wenjie Zhao ◽  
Zhou Fang ◽  
Zuqiang Yang
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Arrival Time ◽  
Trajectory Generation ◽  
Hill Climbing ◽  
Q Learning ◽  
Continuous State ◽  
Multi Agent ◽  
Multiple Unmanned Aerial Vehicles ◽  
Simulation Results ◽  
Multi Uav

A distributed four-dimensional (4D) trajectory generation method based on multi-agent Q learning is presented for multiple unmanned aerial vehicles (UAVs). Based on this method, each vehicle can intelligently generate collision-free 4D trajectories for time-constrained cooperative flight tasks. For a single UAV, the 4D trajectory is generated by the bionic improved tau gravity guidance strategy, which can synchronously guide the position and velocity to the desired values at the arrival time. Furthermore, to optimise trajectory parameters, the continuous state and action wire fitting neural network Q (WFNNQ) learning method is applied. For multi-UAV applications, the learning is organised by the win or learn fast-policy hill climbing (WoLF-PHC) algorithm. Dynamic simulation results show that the proposed method can efficiently provide 4D trajectories for the multi-UAV system in challenging simultaneous arrival tasks, and the fully trained method can be used in similar trajectory generation scenarios.

Coaches estimation method for the athletes (CEM)

1995 ◽  
Author(s):  
Nagykaldi Csaba ◽  
Manohar Singh Badhan
Keyword(s):  
Estimation Method

Theoretical, methodological and practical issues of choice models

2018 ◽  
Vol 1 (1) ◽  
pp. 21-37
Author(s):  
Bharat P. Bhatta
Keyword(s):  
Discrete Choice ◽  
Model Building ◽  
Estimation Method ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Decision Makers ◽  
Model Formulation ◽  
Random Utility Maximization ◽  
The Relationship ◽  
Method Model

This paper analyzes and synthesizes the fundamentals of discrete choice models. This paper alsodiscusses the basic concept and theory underlying the econometrics of discrete choice, specific choicemodels, estimation method, model building and tests, and applications of discrete choice models. Thiswork highlights the relationship between economic theory and discrete choice models: how economictheory contributes to choice modeling and vice versa. Keywords: Discrete choice models; Random utility maximization; Decision makers; Utility function;Model formulation

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