On the Communication Range in Auction-Based Multi-Agent Target Assignment

2011 ◽  
pp. 32-43 ◽  
Author(s):  
Marin Lujak ◽  
Stefano Giordani
Keyword(s):  
Communication Range ◽  
Target Assignment ◽  
Multi Agent

scholarly journals Joint Optimization of Multi-UAV Target Assignment and Path Planning Based on Multi-Agent Reinforcement Learning

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 146264-146272 ◽  
Author(s):  
Han Qie ◽  
Dianxi Shi ◽  
Tianlong Shen ◽  
Xinhai Xu ◽  
Yuan Li ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Joint Optimization ◽  
Target Assignment ◽  
Multi Agent ◽  
Multi Uav

scholarly journals Distributed Control of Multi-Agent Systems With Limited Communication Range in the Fixed Obstacle Environments

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 118259-118268
Author(s):  
Hamid Mahboubi ◽  
Farid Sharifi ◽  
Amir G. Aghdam ◽  
Youmin Zhang
Keyword(s):  
Distributed Control ◽  
Multi Agent Systems ◽  
Communication Range ◽  
Agent Systems ◽  
Limited Communication ◽  
Multi Agent

scholarly journals The Relation Between Communication Range and Controllability of Networked Multi-Agent Systems

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 35901-35907
Author(s):  
Bo Ouyang ◽  
Qiang Ye ◽  
Siddharth Patil ◽  
Cong Wang ◽  
Lu Lu ◽  
...  
Keyword(s):  
Multi Agent Systems ◽  
Communication Range ◽  
Agent Systems ◽  
Multi Agent

A Multiattribute Utility Approach to Target Assignment

2009 ◽  
Author(s):  
Xi Chen ◽  
Ali E. Abbas ◽  
Dusˇan M. Stipanovic´
Keyword(s):  
Utility Function ◽  
Kinematic Model ◽  
Multiattribute Utility ◽  
Control Lyapunov Function ◽  
Control Laws ◽  
Function Design ◽  
Target Assignment ◽  
Conduct Sensitivity Analysis ◽  
Multi Agent ◽  
Utility Approach

This paper introduces the multiattribute utility theory to the control Lyapunov function design framework. As an illustration we focus on the problem of multi-target assignment. With this formulation, we use a global multiattribute utility function as a multivariate objective function that should be minimized for the agents to achieve their objectives. The objectives represent deviations of each agent from specified targets. We provide closed form feedback control laws, based on the multiattribute utility function, for general nonlinear multi-agent system models affine in control. Finally, we present simulation results and conduct sensitivity analysis for two different models that are affine in control; basic kinematic model and nonlinear nonholonomic unicycle model.

scholarly journals Generalized Target Assignment and Path Finding Using Answer Set Programming

2017 ◽  
Author(s):  
Van Nguyen ◽  
Philipp Obermeier ◽  
Tran Cao Son ◽  
Torsten Schaub ◽  
William Yeoh
Keyword(s):  
Scale Up ◽  
Answer Set Programming ◽  
Specific Information ◽  
Path Finding ◽  
Practical Applications ◽  
Target Assignment ◽  
Specific Order ◽  
Multi Agent ◽  
Unequal Number ◽  
Answer Set

In Multi-Agent Path Finding (MAPF), a team of agents needs to find collision-free paths from their starting locations to their respective targets. Combined Target Assignment and Path Finding (TAPF) extends MAPF by including the problem of assigning targets to agents as a precursor to the MAPF problem. A limitation of both models is their assumption that the number of agents and targets are equal, which is invalid in some applications such as autonomous warehouse systems. We address this limitation by generalizing TAPF to allow for (1)~unequal number of agents and tasks; (2)~tasks to have deadlines by which they must be completed; (3)~ordering of groups of tasks to be completed; and (4)~tasks that are composed of a sequence of checkpoints that must be visited in a specific order. Further, we model the problem using answer set programming (ASP) to show that customizing the desired variant of the problem is simple one only needs to choose the appropriate combination of ASP rules to enforce it. We also demonstrate experimentally that if problem specific information can be incorporated into the ASP encoding then ASP based method can be efficient and can scale up to solve practical applications.

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