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Energy-efficient green ant colony optimization for path planning in dynamic 3D environments.

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Title: Energy-efficient green ant colony optimization for path planning in dynamic 3D environments.
Authors: Sangeetha, V.1 (AUTHOR), Krishankumar, R.1 (AUTHOR), Ravichandran, K. S.1 (AUTHOR) ravichandran20962@gmail.com, Kar, Samarjit2 (AUTHOR)
Source: Soft Computing - A Fusion of Foundations, Methodologies & Applications. Mar2021, Vol. 25 Issue 6, p4749-4769. 21p.
Subjects: Ant algorithms, Ant colonies, Metaheuristic algorithms, Energy consumption, Statistical significance
Abstract: With the proliferation in demand for navigation systems for reconnaissance, surveillance, and other day-to-day activities, the development of efficient and robust path planning algorithm is an open challenge. The uncertain and dynamic nature of the real-time scenario imposes a challenge for the autonomous systems to navigate in the environment, avoiding collision with the moving obstacles without compromising on the energy-time trade-off. Motivated by this challenge, an efficient gain-based dynamic green ant colony optimization (GDGACO) metaheuristic has been proposed in this paper. The energy consumption while path planning in a dynamic scenario will be humongous owing to its nature. The proposed algorithm reduces the total energy consumed during path planning through an efficient gain function-based pheromone enhancement mechanism. The memory efficiency of Octrees is incorporated for workspace representation because of its ability to map large 3D environments to limited memory. Comprehensive simulation experiments are conducted to demonstrate the efficacy of GDGACO. Results are analysed through comparison with other methods in terms of path length, computation time, and energy consumed. Also, the results are verified for statistical significance. [ABSTRACT FROM AUTHOR]
Copyright of Soft Computing - A Fusion of Foundations, Methodologies & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="DE" term="%22Ant+algorithms%22">Ant algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Ant+colonies%22">Ant colonies</searchLink><br /><searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+significance%22">Statistical significance</searchLink>
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  Data: With the proliferation in demand for navigation systems for reconnaissance, surveillance, and other day-to-day activities, the development of efficient and robust path planning algorithm is an open challenge. The uncertain and dynamic nature of the real-time scenario imposes a challenge for the autonomous systems to navigate in the environment, avoiding collision with the moving obstacles without compromising on the energy-time trade-off. Motivated by this challenge, an efficient gain-based dynamic green ant colony optimization (GDGACO) metaheuristic has been proposed in this paper. The energy consumption while path planning in a dynamic scenario will be humongous owing to its nature. The proposed algorithm reduces the total energy consumed during path planning through an efficient gain function-based pheromone enhancement mechanism. The memory efficiency of Octrees is incorporated for workspace representation because of its ability to map large 3D environments to limited memory. Comprehensive simulation experiments are conducted to demonstrate the efficacy of GDGACO. Results are analysed through comparison with other methods in terms of path length, computation time, and energy consumed. Also, the results are verified for statistical significance. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Soft Computing - A Fusion of Foundations, Methodologies & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00500-020-05483-6
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        Text: English
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      – SubjectFull: Ant colonies
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      – SubjectFull: Metaheuristic algorithms
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              Text: Mar2021
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