A Prototype of a Low-Cost, Downsizeable, Dynamically Reconfigurable Unit for Robot Swarms

Ribas, Lluís; Izquierdo, M.; Mujal, Jordi; Ramón, Eloi

doi:10.1109/isie.2007.4374944

Cited by 1 publication

(2 citation statements)

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“…There have been many studies on the CAC implementation of mobile robots using various approaches [46,[61][62][63]. The selection of on-board sensors is equally important as the development of CAC navigation algorithm itself.…”

Section: Implementation Issuesmentioning

confidence: 99%

“…In some other methods, multirobots collect the incoming sensor information of every single robot in a team to cooperatively perform coverage in unknown environments. In most cases, cell decomposition method is used to split terrain and ensures complete coverage [46]. Butler [38,39] proposed a distributed cooperative coverage algorithm for multirobot, which performs independently on each robot in a team with a rectilinear environment.…”

Section: Introductionmentioning

confidence: 99%

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Biologically Inspired Intelligence with Applications on Robot Navigation

Luo¹,

Jan²,

Chu³

et al. 2018

Artificial Intelligence - Emerging Trends and Applications

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Biologically inspired intelligence technique, an important embranchment of series on computational intelligence, plays a crucial role for robotics. The autonomous robot and vehicle industry has had an immense impact on our economy and society and this trend will continue with biologically inspired neural network techniques. In this chapter, multiple robots cooperate to achieve a common coverage goal efficiently, which can improve the work capacity, share the coverage tasks, and reduce the completion time by a biologically inspired intelligence technique, is addressed. In many real-world applications, the coverage task has to be completed without any prior knowledge of the environment. In this chapter, a neural dynamics approach is proposed for complete area coverage by multiple robots. A bio-inspired neural network is designed to model the dynamic environment and to guide a team of robots for the coverage task. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting neural equation. Each mobile robot treats the other robots as moving obstacles. Each robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot position. The proposed model algorithm is computationally simple. The feasibility is validated by four simulation studies.

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Section: Implementation Issuesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%