Dynamic Mission Control Method for Multi-UAV System

2014 ◽  
Vol 490-491 ◽  
pp. 942-946 ◽  
Author(s):  
Yan Zhao ◽  
Guo Qing Long ◽  
Shi You Dong
Keyword(s):  
Task Allocation ◽  
Control Method ◽  
Dynamic Environment ◽  
Physical Object ◽  
Market Mechanism ◽  
Dynamic Task ◽  
Dynamic Task Allocation ◽  
Mission Control ◽  
Negotiation Mechanism ◽  
Multi Uav

On the problem of mission control of the multi-UAV system in a dynamic environment, the method of Dynamic Task Allocation and Coordination (DTAC), based on the combination of market mechanism and alliance recruitment, is proposed. On the basis of the DTAC model of multi-UAV system, constructed with the application of market negotiation, the physical object of the market negotiation mechanism is substituted by that of alliance recruitment. Furthermore, the recruiter is empowered to coordinate within the alliance, which reduces the auction times and increases the efficiency of DTAC. The result of simulation shows that the method can effectively solve the problem of dynamic mission control of the Multi-UAV system.

scholarly journals Auction-Based Consensus of Autonomous Vehicles for Multi-Target Dynamic Task Allocation and Path Planning in an Unknown Obstacle Environment

2021 ◽  
Vol 11 (11) ◽  
pp. 5057
Author(s):  
Wan-Yu Yu ◽  
Xiao-Qiang Huang ◽  
Hung-Yi Luo ◽  
Von-Wun Soo ◽  
Yung-Lung Lee
Keyword(s):  
Path Planning ◽  
Task Allocation ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Dynamic Task ◽  
The Core ◽  
Dynamic Task Allocation ◽  
Vehicle Technology ◽  
Future Work ◽  
Complicated Task

The autonomous vehicle technology has recently been developed rapidly in a wide variety of applications. However, coordinating a team of autonomous vehicles to complete missions in an unknown and changing environment has been a challenging and complicated task. We modify the consensus-based auction algorithm (CBAA) so that it can dynamically reallocate tasks among autonomous vehicles that can flexibly find a path to reach multiple dynamic targets while avoiding unexpected obstacles and staying close as a group as possible simultaneously. We propose the core algorithms and simulate with many scenarios empirically to illustrate how the proposed framework works. Specifically, we show that how autonomous vehicles could reallocate the tasks among each other in finding dynamically changing paths while certain targets may appear and disappear during the movement mission. We also discuss some challenging problems as a future work.

scholarly journals A multi-responsibility–oriented coalition formation framework for dynamic task allocation in mobile–distributed multi-agent systems

2018 ◽  
Vol 15 (6) ◽  
pp. 172988141881303 ◽  
Author(s):  
Bing Xie ◽  
Xueqiang Gu ◽  
Jing Chen ◽  
LinCheng Shen
Keyword(s):  
Coalition Formation ◽  
Task Allocation ◽  
Game Model ◽  
Multi Agent Systems ◽  
Coalition Game ◽  
Task Execution ◽  
Agent Systems ◽  
Dynamic Task ◽  
Dynamic Task Allocation ◽  
Multi Agent

In this article, we study a problem of dynamic task allocation with multiple agent responsibilities in distributed multi-agent systems. Agents in the research have two responsibilities, communication and task execution. Movements in agent task execution bring changes to the system network structure, which will affect the communication. Thus, agents need to be autonomous on communication network reconstruction for good performance on task execution. First, we analyze the relationships between the two responsibilities of agents. Then, we design a multi-responsibility–oriented coalition formation framework for dynamic task allocation with two parts, namely, task execution and self-adaptation communication. For the former part, we integrate our formerly proposed algorithm in the framework for task execution coalition formation. For the latter part, we develop a constrained Bayesian overlapping coalition game model to formulate the communication network. A task-allocation efficiency–oriented communication coalition utility function is defined to optimize a coalition structure for the constrained Bayesian overlapping coalition game model. Considering the geographical location dependence between the two responsibilities, we define constrained agent strategies to map agent strategies to potential location choices. Based on the abovementioned design, we propose a distributed location pruning self-adaptive algorithm for the constrained Bayesian overlapping coalition formation. Finally, we test the performance of our framework, multi-responsibility–oriented coalition formation framework, with simulation experiments. Experimental results demonstrate that the multi-responsibility oriented coalition formation framework performs better than the other two distributed algorithms on task completion rate (by over 9.4% and over 65% on average, respectively).

scholarly journals Analysis of Dynamic Task Allocation in Multi-Robot Systems

2006 ◽  
Author(s):  
Kristina Lerman ◽  
Chris Jones ◽  
Aram Galstyan ◽  
Maja J. Mataric
Keyword(s):  
Task Allocation ◽  
Dynamic Task ◽  
Dynamic Task Allocation ◽  
Robot Systems ◽  
Multi Robot
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