Low-cost thermal explorer robot using a hybrid neural networks and intelligent bug algorithm model

Melo, Willian De; Jorge, David Calhau; Marques, Vinicius Abrão da Silva

doi:10.1504/ijcat.2021.116013

Cited by 1 publication

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“…When an emergency occurs on a path, the potential solution can be immediately changed to another option, thus addressing the inefficiencies of traditional path planning. Different from the scheme of a bug algorithm robot walking around obstacles when encountering obstacles and walking along a straight line without encountering obstacles [ 32 ], the multimodal path planning strategy adopts the segmented method as described in Section 4.1 . Each node position of the segmented (

) corresponds to a dimension of the variable; then the paraPA algorithm is used to find multiple paths.…”

Section: Problem Formulation and The Strategies For Path Planningmentioning

confidence: 99%

A Novel Swarm Intelligence Algorithm with a Parasitism-Relation-Based Structure for Mobile Robot Path Planning

Hui

Gao

Shen

et al. 2023

Sensors

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A multi-swarm-evolutionary structure based on the parasitic relationship in the biosphere is proposed in this paper and, according to the conception, the Para-PSO-ABC algorithm (ParaPA), combined with merits of the modified particle swarm optimization (MPSO) and artificial bee colony algorithm (ABC), is conducted with the multimodal routing strategy to enhance the safety and the cost issue for the mobile robot path planning problem. The evolution is divided into three stages, where the first is the independent evolutionary stage, with the same evolution strategies for each swarm. The second is the fusion stage, in which individuals are evolved hierarchically in the parasitism structure. Finally, in the interaction stage, a multi-swarm-elite strategy is used to filter the information through a predefined cross function among swarms. Meanwhile, the segment obstacle-avoiding strategy is proposed to accelerate the searching speed with two fitness functions. The best path is selected according to the performance on the safety and consumption issues. The introduced algorithm is examined with different obstacle allocations and simulated in the real routing environment compared with some typical algorithms. The results verify the productiveness of the parasitism-relation-based structure and the stage-based evolution strategy in path planning.

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) corresponds to a dimension of the variable; then the paraPA algorithm is used to find multiple paths.…”

Section: Problem Formulation and The Strategies For Path Planningmentioning

confidence: 99%

A Novel Swarm Intelligence Algorithm with a Parasitism-Relation-Based Structure for Mobile Robot Path Planning

Hui

Gao

Shen

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

Low-cost thermal explorer robot using a hybrid neural networks and intelligent bug algorithm model

Cited by 1 publication

References 0 publications

A Novel Swarm Intelligence Algorithm with a Parasitism-Relation-Based Structure for Mobile Robot Path Planning

A Novel Swarm Intelligence Algorithm with a Parasitism-Relation-Based Structure for Mobile Robot Path Planning

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