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Ruiz-Sánchez R, Arencibia-Jorge R, Tagüeña J, Jiménez-Andrade JL, Carrillo-Calvet H. Exploring research on ecotechnology through artificial intelligence and bibliometric maps. Environ Sci Ecotechnol 2024; 21:100386. [PMID: 38328508 PMCID: PMC10848037 DOI: 10.1016/j.ese.2023.100386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 12/28/2023] [Accepted: 12/29/2023] [Indexed: 02/09/2024]
Abstract
Ecotechnology, quintessential for crafting sustainable socio-environmental strategies, remains tantalizingly uncharted. Our analysis, steered by the nuances of machine learning and augmented by bibliometric insights, delineates the expansive terrain of this domain, elucidates pivotal research themes and conundrums, and discerns the vanguard nations in this field. Furthermore, we deftly connect our discoveries to the United Nations' 2030 Sustainable Development Goals, thereby accentuating the profound societal ramifications of ecotechnology.
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Affiliation(s)
- Ricardo Ruiz-Sánchez
- Unidad Profesional Interdisciplinaria de Ingeniería Palenque (UPIIP), Instituto Politécnico Nacional, Palenque, Chiapas, CP 29960, Mexico
- Complexity Sciences Center, National Autonomous University of Mexico, Circuito Centro Cultural s/n, Coyoacan, 04510, Mexico City, Mexico
| | - Ricardo Arencibia-Jorge
- Complexity Sciences Center, National Autonomous University of Mexico, Circuito Centro Cultural s/n, Coyoacan, 04510, Mexico City, Mexico
| | - Julia Tagüeña
- Complexity Sciences Center, National Autonomous University of Mexico, Circuito Centro Cultural s/n, Coyoacan, 04510, Mexico City, Mexico
- Institute of Renewable Energies (IER), National Autonomous University of Mexico, Priv. Xochicalco s/n, Col. Centro, Temixco, Morelos, CP 62580, Mexico
| | - José Luis Jiménez-Andrade
- Complexity Sciences Center, National Autonomous University of Mexico, Circuito Centro Cultural s/n, Coyoacan, 04510, Mexico City, Mexico
- Faculty of Sciences, National Autonomous University of Mexico, Circuito Centro Cultural s/n, Coyoacan, 04510, Mexico City, Mexico
| | - Humberto Carrillo-Calvet
- Complexity Sciences Center, National Autonomous University of Mexico, Circuito Centro Cultural s/n, Coyoacan, 04510, Mexico City, Mexico
- Faculty of Sciences, National Autonomous University of Mexico, Circuito Centro Cultural s/n, Coyoacan, 04510, Mexico City, Mexico
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Lei T, Chintam P, Luo C, Liu L, Jan GE. A Convex Optimization Approach to Multi-Robot Task Allocation and Path Planning. Sensors (Basel) 2023; 23:s23115103. [PMID: 37299829 DOI: 10.3390/s23115103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 05/13/2023] [Accepted: 05/22/2023] [Indexed: 06/12/2023]
Abstract
In real-world applications, multiple robots need to be dynamically deployed to their appropriate locations as teams while the distance cost between robots and goals is minimized, which is known to be an NP-hard problem. In this paper, a new framework of team-based multi-robot task allocation and path planning is developed for robot exploration missions through a convex optimization-based distance optimal model. A new distance optimal model is proposed to minimize the traveled distance between robots and their goals. The proposed framework fuses task decomposition, allocation, local sub-task allocation, and path planning. To begin, multiple robots are firstly divided and clustered into a variety of teams considering interrelation and dependencies of robots, and task decomposition. Secondly, the teams with various arbitrary shape enclosing intercorrelative robots are approximated and relaxed into circles, which are mathematically formulated to convex optimization problems to minimize the distance between teams, as well as between a robot and their goals. Once the robot teams are deployed into their appropriate locations, the robot locations are further refined by a graph-based Delaunay triangulation method. Thirdly, in the team, a self-organizing map-based neural network (SOMNN) paradigm is developed to complete the dynamical sub-task allocation and path planning, in which the robots are dynamically assigned to their nearby goals locally. Simulation and comparison studies demonstrate the proposed hybrid multi-robot task allocation and path planning framework is effective and efficient.
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Affiliation(s)
- Tingjun Lei
- Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS 39762, USA
| | - Pradeep Chintam
- Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS 39762, USA
| | - Chaomin Luo
- Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS 39762, USA
| | - Lantao Liu
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN 47408, USA
| | - Gene Eu Jan
- Department of Electrical Engineering, National Taipei University, New Taipei City 23741, Taiwan
- Tainan National University of the Arts, Tainan City 72045, Taiwan
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