Robot Path Planning Using an Improved Genetic Algorithm with Ordered Feasible Subpaths

Tan, Xianfeng; Lei, Deming; Wu, Dongrui; Zheng, Li

doi:10.1109/cac.2018.8623634

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…Another improved version of GA is introduced by Tan et al (Tan et al, 2018) called Improved GA with Ordered Feasible Subpaths (IGAOFS). In particular, the proposed GA was equipped with mutation and simplification operands to increase the feasibility and efficiency of the founded solutions.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Computational Intelligence Algorithm for Autonomous Handling of COVID-19 Pandemic Emergency in Smart Cities

Abdel‐Basset

Eldrandaly

Shawky

et al. 2022

Sustainable Cities and Society

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Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre -including this research content -immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

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Section: Literature Reviewmentioning

confidence: 99%

Hybrid Computational Intelligence Algorithm for Autonomous Handling of COVID-19 Pandemic Emergency in Smart Cities

Abdel‐Basset

Eldrandaly

Shawky

et al. 2022

Sustainable Cities and Society

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Improved Genetic Algorithm in a Static Environment for the Robotic Path Planning Problem

Mankudiyil,

Dornberger,

Hanne

2024

Data Science and Applications

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Multi-UAV cooperative Route planning based on decision variables and improved genetic algorithm

Zhang

et al. 2021

J. Phys.: Conf. Ser.

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In order to reduce the threat cost of multi-UAV coordination, improve the effective flight time and the mission success rate, a path planning model was designed for the effectiveness and real-time performance of multi-UAV track coordination. In this paper, based on the path planning model under the condition of non-interference, the model of minimum cost flight path of multi-UAV based on time synergy is given, and a time-sequence-space coordination model is proposed to meet the requirements of electronic jamming, mission confusion and radar illumination. The decision variables of range and threat are introduced into the collaborative function of track cost, and by adjusting the cost functions and collaborative variables in real time, the path planning model was optimized for unmanned support aircraft in coordination with multiple unmanned aircraft mission, at the same time, the genetic algorithm is improved by combining the adaptive crossover rate and mutation rate, and the optimization of track collaborative solution process is realized. Finally, the effectiveness of track coordination model of multi-UAV based on decision variables and improved genetic algorithm is verified by semi-physical simulation.

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Robot Path Planning Using an Improved Genetic Algorithm with Ordered Feasible Subpaths

Cited by 5 publications

References 13 publications

Hybrid Computational Intelligence Algorithm for Autonomous Handling of COVID-19 Pandemic Emergency in Smart Cities

Hybrid Computational Intelligence Algorithm for Autonomous Handling of COVID-19 Pandemic Emergency in Smart Cities

Improved Genetic Algorithm in a Static Environment for the Robotic Path Planning Problem

Multi-UAV cooperative Route planning based on decision variables and improved genetic algorithm

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