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Yibei Guo, Yijiang Pang, Joseph Lyons, Michael Lewis, Katia Sycara and Rui Liu
Due to the complexity of real-world deployments, a robot swarm is required to dynamically respond to tasks such as tracking multiple vehicles and continuously searching for victims. Frequent task assignments eliminate the need for system calibration time...
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Dan Xu, Yunxiao Guo, Zhongyi Yu, Zhenfeng Wang, Rongze Lan, Runhao Zhao, Xinjia Xie and Han Long
Flocking for fixed-Wing Unmanned Aerial Vehicles (UAVs) is an extremely complex challenge due to fixed-wing UAV?s control problem and the system?s coordinate difficulty. Recently, flocking approaches based on reinforcement learning have attracted attenti...
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Sarab AlMuhaideb, Ameur Touir, Reem Alshraihi, Najwa Altwaijry and Safwan Qasem
Flocking is one of the swarm tasks inspired by animal behavior. A flock involves multiple agents aiming to achieve a goal while maintaining certain characteristics of their formation. In nature, flocks vary in size. Although several studies have focused ...
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Charles Coquet, Andreas Arnold and Pierre-Jean Bouvet
We describe and analyze the Local Charged Particle Swarm Optimization (LCPSO) algorithm, that we designed to solve the problem of tracking a moving target releasing scalar information in a constrained environment using a swarm of agents. This method is i...
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Mohammad Jafari and Hao Xu
In this paper, a biologically-inspired distributed intelligent control methodology is proposed to overcome the challenges, i.e., networked imperfections and uncertainty from the environment and system, in networked multi-Unmanned Aircraft Systems (UAS) f...
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