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

Author(s):  
Marin Lujak ◽  
Stefano Giordani
IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 146264-146272 ◽  
Author(s):  
Han Qie ◽  
Dianxi Shi ◽  
Tianlong Shen ◽  
Xinhai Xu ◽  
Yuan Li ◽  
...  

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 118259-118268
Author(s):  
Hamid Mahboubi ◽  
Farid Sharifi ◽  
Amir G. Aghdam ◽  
Youmin Zhang

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

Author(s):  
Xi Chen ◽  
Ali E. Abbas ◽  
Dusˇan M. Stipanovic´

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.


Author(s):  
Van Nguyen ◽  
Philipp Obermeier ◽  
Tran Cao Son ◽  
Torsten Schaub ◽  
William Yeoh

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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